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16 CHAPTER 2 LITERATURE REVIEW 2.1. REPORTED WORKS ON IMAGE ENHANCEMENT USING EVOLVED OPERATORS [6] Presented a circuit representation technique for automated circuit design. The applications are mainly in the areas of classification and control when complete circuit design is applied. There are also some examples of circuit parameter tuning. It is based on digital gate level technology using GA as the evolutionary algorithm. However, promising results are given for analog designs, where evolution is used to find optimal parameters for analog components. [14] Proposed image Filter Design with Evolvable Hardware. It introduces a new approach to automatic design of image filters for a type of noise. The approach employs evolvable hardware at simplified functional level and produces circuits that outperform conventional designs. If an image is available both with and without noise, the whole process of filter design can be done automatically, without the influence of a designer. [19] Proposed virtual reconfigurable Circuits for Real-World Applications of Evolvable Hardware. The evolved image filters use Cartesian genetic programming (CGP) applied at the functional level. Furthermore, the hardware implementation of CGP was proposed for FPGAs. However, in his approach image filters were evolved only by using a virtual reconfigurable circuit simulated in software that could eventually be implemented on the top of a conventional FPGA. [20] Presented an accelerated image processing architecture on FPGAs with parallel processing elements. A convolution operation is implemented in

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CHAPTER 2

LITERATURE REVIEW

2.1. REPORTED WORKS ON IMAGE ENHANCEMENT USINGEVOLVED OPERATORS

[6] Presented a circuit representation technique for automated circuit

design. The applications are mainly in the areas of classification and control

when complete circuit design is applied. There are also some examples of circuit

parameter tuning. It is based on digital gate level technology using GA as the

evolutionary algorithm. However, promising results are given for analog designs,

where evolution is used to find optimal parameters for analog components.

[14] Proposed image Filter Design with Evolvable Hardware. It

introduces a new approach to automatic design of image filters for a type of

noise. The approach employs evolvable hardware at simplified functional level

and produces circuits that outperform conventional designs. If an image is

available both with and without noise, the whole process of filter design can be

done automatically, without the influence of a designer.

[19] Proposed virtual reconfigurable Circuits for Real-World Applications

of Evolvable Hardware. The evolved image filters use Cartesian genetic

programming (CGP) applied at the functional level. Furthermore, the hardware

implementation of CGP was proposed for FPGAs. However, in his approach

image filters were evolved only by using a virtual reconfigurable circuit

simulated in software that could eventually be implemented on the top of a

conventional FPGA.

[20] Presented an accelerated image processing architecture on FPGAs

with parallel processing elements. A convolution operation is implemented in

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FPGA to be applied for real-time image processing. It has also been proposed to

evolve image filters in reconfigurable logic.

[31] Presented a universal noise removal algorithm with an impulse

detector. An important problem of image processing is to effectively remove

noise from an image while keeping its features. There are two noise models that

can be used to represent most noise in images: additive Gaussian noise and

impulse noise. Additive Gaussian noise is characterized by adding to each image

pixel a value with a zero-mean Gaussian distribution. Such noise is usually

introduced during image acquisition.

[37] Proposed digital Filter Design using Evolvable Hardware Chip for

Image Enhancement. Images acquired through modern cameras may be

contaminated by a variety of noise sources (e.g. photon or on chip electronic

noise) and also by distortions such as shading or improper illumination.

Therefore, a pre-processing unit has to be incorporated before recognition to

improve image quality.

[40] Presented reducing the area on a chip using a bank of evolved filters.

It is applying EA in image filtering can be separated into the following two

categories: (i) parameter tuning for improving performance of the existing filters,

(ii) designing a new structure filter by EA. Both types of works have an explicit

target: an optimal filter circuit. Comparing with these works, the proposed image

filter based approach possesses the different features: noise cancellation is

performed only on the noise candidates and noise free pixels will not be

changed. Thus, more image edge detail can be preserved and computation effort

will be reduced.

[41] Presented evolvable reconfigurable hardware framework for edge

detection. Systems on Reconfigurable Chips contain rich resources of logic,

memory and processor cores on the same fabric. This platform is suitable for

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implementation of Evolvable Reconfigurable Hardware Architectures (ERHA).

This architecture is a suitable candidate for implementation of early-processing

stage operators of image processing such as filtering and edge detection.

[49] Presented reconfigurable hardware implementations for lifting-based

DWT image processing algorithms. This scheme presents advantages over the

convolution-based approach, for instance it is very suitable for parallelization.

This paper presents two new FPGA-based parallel implementations of the DWT

lifting-based scheme, (i) uses pipelining, parallel processing and data to increase

the speed up of the algorithm, and (ii) a controller is introduced to deploy

dynamically a suitable number of clones according to the available hardware

resources on a targeted environment.

[59] Proposed EHW Architecture for Design of Adaptive Median Filter

for Noise Reduction. A new technique for the design of Adaptive Median Filter

within an Evolvable hardware framework, using genetic algorithm (GA), aimed

at removing the impulse noise from the image and reducing distortion in the

image is presented. It reduces the number of generations required to provide time

bound optimal filter configuration and to improve the quality of the filter

designed.

[62] Proposed application of partial reconfiguration of FPGAs in image

processing. FPGA based hardware accelerators have been more and more widely

used in different kind of applications. As compared to other solutions and the

direct hardware implementation, the advantage of the FPGA devices is their

flexibility that arises from their programmable nature. In addition to this, some

FPGA devices also support partial dynamic reconfiguration.

[86] Presented an efficient Image Noise Removal and Enhancement

Method. It presents a new method to remove noise and enhance the image with

the help of partial unsharp masking and conservative smoothing. In this method,

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unsharp masking is applied in partial way for detection of the edges and

boundary lines in the image and then a conservative smoothing operation is

applied on the selected areas to remove undesirable edges which represents the

salt and pepper noise.

[90] Proposed image enhancement based on Improved Genetic Algorithm

and Lifting Wavelet Method. This algorithm improves crossover operation

algorithm and utilizes average displacement method for mutation operation.

Probabilities of crossover and mutation are selected adaptively. It decided the

fitness function and has implemented multi-thread design. The algorithm

optimizes the prediction and updating operator of lifting wavelet by means of

genetic algorithm.

[99]Presented architecture for binary mathematical morphology

reconfigurable by genetic programming. The mathematical morphology supplies

powerful tools for low level image analysis, with applications in robotic vision,

visual inspection, medicine, texture analysis and many other areas. Many of the

mentioned applications require dedicated hardware for real time execution. The

development of a novel reconfigurable hardware using logical and

morphological instructions generated automatically by a linear approach based

on genetic programming is proposed.

[100] Presented reconfigurable hardware objects for image processing on

FPGAs. Embedded systems require high level of abstraction is required during

the development process. High abstraction methods simplify implementation of

complex computation systems and shorten the time to market. It represents an

implementation of a graphic computing element (GCE) which can be used as a

runtime parameterized building block in image processing applications in

FPGAs.

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[92] Proposed an edge Enhancement Algorithm using Wavelet Transform

for Automatic Edge Detection in Synthetic aperture radar (SAR) Images. It

presents a novel technique for automatic edge enhancement and detection in

SAR images. The characteristics of SAR images justify the importance of an

edge enhancement step prior to edge detection. Therefore, it presents a robust

and unsupervised edge enhancement algorithm based on a combination of

wavelet coefficients at different scales.

[94] Proposed image enhancement and denoising based on structure

self-similarity and wavelet transform coefficients. Image denoise is a very

important method in image quality improvement and the image quality

evaluation is another important aspect in image processing. In this work, a novel

enhancement algorithm based on wavelet transform (WT) and structure self-

similarity (SSS) is introduced, which was attested to algorithm effective for

enhancement and confining random noise.

[110] Proposed an efficient run-time task allocation in reconfigurable

multiprocessor system-on-chip with network-on-chip. Due to the advancement

of VLSI (Very Large Scale Integrated Circuits) technologies, it can put more

cores on a chip, resulting in the emergence of a multicore embedded system. It

also brings great challenges to the traditional parallel processing in improving

the performance of the system with increased number of cores.

2.2. REPORTED WORKS ON SURFACE ROUGHNESS

[32] Presented a problem of emphasizing the features of surface

roughness by Discrete Wavelet Transform. The detection of the roughness

features by means of the 3D reconstruction, based on photometric stereo

techniques, an important problem is the elimination of the brightness variation

due to different light conditions which can alter the response. The level of

brightness depends on many factors as well as the homogeneity of reflection

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properties of the material or its physical continuity and the surface smoothness or

roughness.

[29] Presented an application of digital image magnification for surface

roughness evaluation using machine vision. A machine vision system has been

utilized to capture the images and then the quantification of the surface

roughness of machined surfaces (ground, milled and shaped) is done by the

application of regression analysis. Subsequently, original images have been

magnified using Cubic Convolution interpolation technique and improved (edge

enhancement) through Linear Edge Crispening algorithm.

[63] Presented restoration of blurred images for surface roughness

evaluation using machine vision. The surface roughness of uniformly moving

machined surface (grinding, milling) using machine vision technique is

evaluated. In the case of moving surfaces the images are likely to blur due to the

relative motion between the CCD camera and the object to be captured. Hence,

the degraded image has to be restored by removing distortion due to motion

before subsequent analysis.

In [2], four methods that yield mathematical measures to analyze the

precision of surfaces of manufactured parts is investigated. In terms of precision

manufacturing, measures provide the potential of detecting and improving

surface errors in high-precision product geometry. The average energy is given

by the eigenvalues of the covariance matrix. The covariance matrix contains the

statistical properties of the original data set. In [2], they investigate the feasibility

of finding characteristic measures of precision for precision-ground surfaces.

[3] Includes intuitive properties like roughness, granulation and

regularity. To obtain features which reflect scale-dependent properties, one can

extract a feature from each sub image separately. The transforms retain

localization in both space and frequency, which makes it easy to compute

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multiscale features locally. Thus rotation invariant features, which preferably

still reflect the anisotropy, need to be constructed. Colour images are typically

represented by RGB tristimulus values which correspond to three colour bands.

A straight forward way to process colour textures is by performing a gray level

decomposition on every component image. They have given an overview of the

application of wavelet multi resolution image analysis to texture.

In [12] a laser based system was used to scan the surfaces of 6 steel

sheets. The resulting waveforms were pre-processed and then they were

represented by a set of feature vectors. The light which is reflected contains

information regarding the surface profile of the surface. Then the experimental

procedures will be outlined and the results obtained will be discussed. [12] is due

to the fact that Ra is a measure of amplitude while the variance is a measure of

signal power. In [12], study a commercial optical profilometer was used to scan

the surfaces of steel sheets with 6 different known average surface roughness

(Ra) values.

Standard roughness measurement procedures depend heavily on stylus

instruments, which have only limited flexibility in handling different parts. [64]

is organized as follows. The definition of surface roughness in manufacturing

fields is first described more formally. There are various simple surface

roughness amplitude parameters such as roughness average, root mean square

roughness, and maximum peak to valley roughness, etc. Various spindle speeds,

feed rates, and depths of cut were tested. During the machining, an accelerometer

sensor was used to measure the vibrations. The aim of modeling the end milling

process is predicting the surface roughness of a work-piece machined.

Conventional stylus techniques, though powerful, have their own

limitations. These include the resolution of the stylus, and the damage caused by

the moving diamond stylus when tracing profiles on soft materials. The

diffraction pattern for a rectangular aperture, which is normally illuminated with

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a plane wave, is concentrated principal lying two directions coinciding with the

sides of the aperture, and in each of these directions it corresponds to the width

of the aperture in that direction [1]. From [1], it was observed that the optical

diffraction technique gave good results for turned components of medium

roughness, in spite of the limitations cited above. The use of a helium-neon laser

beam of smaller diameter and a very smooth knife edge could improve the

results further.

With hard turning, which is an attractive alternative to existing grinding

processes, surface quality is of great importance. Signal processing techniques

were used to relate work piece surface topography to the dynamic behaviour of

the machine tool [45]. Hard turning is an established industry process in industry

for finish machining of a wide range of hardened steel work pieces. Hard turning

allows manufacturers to simplify their processes and still achieve the desired

surface finish quality. From [45] they observed that the work piece topography

generated by hard turning is affected by the feed rate, macro tool geometry,

micro tool geometry (tool wear), and machine tool vibrations, etc. The frequency

components of the profile correspond to these factors.

[15] Reports that the deeper a valley, the darker the corresponding pixel,

the higher a peak, the brighter the corresponding area in the image. This

approach is commonly used to describe the continuous wavelet transform (CWT)

for which the mother wavelet can be explicitly expressed. Hence, the wavelet

toolbox allows the decomposition of surfaces into form, waviness and roughness

components well appreciated by mechanical engineers. Those features can also

be quantified according to both the shape of the corresponding peak and its

height. The standard wavelet transform allows separating the different frequency

components of an image. Indeed, characterization by FNWT seems to be a

promising strategy in the field of surface roughness characterization.

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[46] Concluded that CWT can be useful for the analysis of the roughness

products generated by cutting and abrasive machining processes. This situation

leads to the conclusion that other tools must be used for proper analysis of non

stationary profiles. The sample length was set to 0.8 mm and measurement

length to 4.8 mm. Mean values and confidence intervals of vertical and hybrid

parameters values are decreasing with every step of technological process, but

the horizontal parameters change their values in a different way. The results

reported in [46] are also confirmed by CWT matrix with the use of wavelet

“Mexican hat”. CWT using basic Morlet wavelet allows evaluation of the length

of profile constituent wavelets but information about their amplitudes is not

precise.

The work in [7] is based upon wavelets theory, a novel reference for

evaluating surface roughness is proposed here, wherein the surface roughness

can be separated from the actual surface profile f(t). With the rapid development

of high technology, the quality requirement of many manufactured surfaces are

getting more and more strict in the fields of machine building, electronics,

optics, and biomedical engineering, even in domestic industry; therefore, how to

evaluate the surface feature reasonably is becoming increasingly important.

From [7] they observed that the wavelet references are natural and smooth lines

or surfaces without algebraic expression. It is unique. The evaluation precision

of surface roughness by wavelet reference could be higher than that by the

classic reference lines. They are smooth arithmetical mean lines of the profile.

In [8], the paper proposes a new strategy for surface roughness analysis

and characterization based on wavelets. A three-step algorithmic proposed to

perform a task of surface roughness discrimination between surface texture

images coming from eight different engineering processes. Real samples coming

from British Standards roughness comparison specimens were measured in 2D

using an optical measurement system .All the measured surfaces had been

manufactured to have the same arithmetic mean deviation of the surface

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roughness value Ra 0:8lm. In [8] it can be seen that the three texture scanning

methods offer good clustering efficiency especially when using a clustering

method based on the cluster analysis. To be more precise, it can be seen that

scanning by CWT and standard DWT gives better performances. This comes

from the fact that the scaled DWT scanning is not orientation selective and hence

less efficient for texture analysis where texture orientation is a point of

importance.

2.3 REPORTED WORKS ON IMAGE DENOISING AND WAVELET

TRANSFORM

[4] Presented wavelet based image denoising using a Markov Random

Field a priori model. Hidden Markov Models (HMM) models are efficient in

capturing inter-scale dependencies, whereas Random Markov Field models are

more efficient to capture intrascale correlations. The complexity of local

structures is not well described by Random Markov Gaussian densities whereas

Hidden Markov Models can be used to capture higher order statistics.

[11] Proposed that the multiscale Canny-Deriche operator gives the best

performance of all models and they evaluate the performance along with pre-

processing techniques using wavelet transform applied to face image as follows:

The linear auto-associate model applied to face images is presented. Since,

autoassociators are generally interpreted as content addressable memories; their

performance is evaluated by comparing the output of the system with a test

pattern which can be a copy or a degraded version of one of the patterns

previously learned by the system.

[30] Presented salt-and pepper noise removal by median-type noise

detectors and detail preserving regularization. It was reported that

malfunctioning pixels in camera sensors, faulty memory locations in hardware or

transmission of the image in a noisy channel are some of the common causes for

impulse noise.

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[39] Presented thresholded Weighted Median Filters for Ringing

Reduction in Processed Images. It presents how thresholded weighted median

filters (WMFs) can significantly improve visual as well as objective quality of

images affected by ringing. Ringing is identified by its structural properties and

WMFs are chosen according to these structures. Since WMFs are concerned with

the relative values of their input but do not consider its absolute values, a limited

at the filter output is explicitly required.

[50] Proposed application of two-dimension wavelet transform in image

process of pets in stored grain. The wavelet transform is the localization analysis

of time and frequency and it can multi-scale refine the signal by calculating of

flex and transition. It presents a method of using the wavelet transform to detect

the image of pests in stored grain edge based on the multi-scale analysis of the

wavelet transform in the image processing field. The method acquires the

information of the image edge by detecting the image local maxima of the two-

dimension wavelet transform.

[53] Proposed de-noising of natural images corrupted by Gaussian noise

using wavelet techniques and is very effective because of its ability to capture

the energy of a signal in few energy transform values. Investigates the suitability

of different wavelet bases and the size of different neighbourhood on the

performance of image de-noising algorithms in terms of PSNR. The next level of

wavelet transform is applied to the low frequency sub band image LL only. It is

a shrink or kill rule. However, there was a slight improvement in the PSNR of

the reconstructed image using wiener filtering. The de-noised image was

sometimes unacceptably blurred and lost some details. The different wavelet

bases are used in all methods. It was found that fourth level decomposition gave

optimum results.

[52] Presented details preserving median based filter for impulse noise

removal in digital images. A new nonlinear filter called detail preserving median

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based filter for removing salt and pepper noise and random valued impulse noise

with edge and detail preservation is presented. The proposed method first detects

the impulse pixel based on threshold values and then the corrupted pixels are

replaced by the median value of the uncorrupted pixels in the filtering window.

[54] Presented simple adaptive median filter for the removal of impulse

noise from highly corrupted images. It presents a simple, yet efficient way to

remove impulse noise from digital images. This novel method comprises to

detect the impulse noise in the image. It is based on the intensity values, the

pixels are roughly divided into two classes, which are “noise-free pixel” and

“noise pixel”. Then, stage is to eliminate the impulse noise from the image.

[55] Presented a new kind of weighted median filtering algorithm used

for image Processing. The new algorithm first determines noise points in image

through noise detection, then adjusts the size of filtering window adaptively

according to number of noise points in window, the pixel points in the filtering

window are grouped adaptively by certain rules and gives corresponding weight

to each group of pixel points according to similarity, finally the noise detected

are filtering-treated by means of weighted median filtering algorithm.

[58] Proposed an Improved Switching Median Filter for Impulse Noise

Removal. An Improved progressive switching median filter proposed for salt-

and-pepper impulse noise removal from digital images. Results of comparative

analysis of this algorithm with other filters for impulse noise removal show a

high efficiency of this approach relatively to other ones.

[57] Proposed the effect of Input Limiting on Linear and Nonlinear

Filters for the Removal of Impulsive Noise. It investigates the effects of input

limiting on the performance of linear and nonlinear filters when the input signal

is contaminated by impulsive noise modeled as an alphastable random process.

The nonlinear filters chosen are from the median family filters: median and

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recursive median filter and Wiener filter from the linear filter group. The

hypothesis being tested in this paper is that front-end limiting will increased the

performance of median filter when the noise was extremely impulsive.

[61] Presented an efficient non-linear cascade filtering algorithm for

Removal of High Density Salt and Pepper Noise in Image and Video sequence.

This method consists of two stages to enhance the filtering. (i) Decision based

Median Filter (DMF) which is used to identify pixels likely to be contaminated

by salt and pepper noise and replaces them by the median value.

(ii) Unsymmetric Trimmed Filter, either Mean Filter (UTMF) or Midpoint Filter

(UTMP) which is used to trim the noisy pixels in an unsymmetrical manner and

processes with the remaining pixels.

[74] Presented video denoising Using Motion Compensated 3-D Wavelet

Transform with Integrated Recursive Temporal Filtering. The motion-

compensated temporal wavelet transform is first performed on a sliding window

of video frames consisting of previously denoised frames and the current noisy

frame. The 2-D spatial wavelet transform is then performed on the temporal sub

band frames, thus, realizing a 3-D wavelet transform.

[75] Proposed image de-noising algorithm study and realization based on

wavelet analysis. It collects blur image with missing information from an

imaging system. The algorithm based on wavelet analyses does not need any

transcendental information of the image or the image size to estimate the de-

nosing limits and even does not need the information of square difference .It has

a function to reduce image noise blindly.

[85] Presented an effective adaptive median filter algorithm for removing

salt & pepper noise in images. It solved the problem of the simplified Pulse

Coupled Neural Network model in image filtering. The simplified model is

proved to fail to detect pepper noise using the method of reduction ad absurdum

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and the model is improved using the method of divide and rule. Finally, the

adaptive median filter algorithm is achieved by detecting the pollution level of

the image.

[73] Presented application of modified adaptive median filter for impulse

noise. In this paper, based on the statistical features of the image, a modified

adaptive median filter (MAMF) for removal of impulse noise, especially for the

high-density impulse noises is proposed. To avoid the adaptive median filter

(AMF) and the adaptive threshold median filter (ATMF), this method has been

designed by combining the AMF with the Decision-Based Algorithm (DBA).

[69] Presented the mathematical Programming Problem of Total

Variation Image Denoising Model. Image denoising is an image processing

problem, which has a wide use. Total Variation image denoising model is one of

the best models at the present time. According to its features, it proposes three

mathematical programming models with guaranteed global optimum.

[71] Presented the Image Edge Detection Algorithm based on Wavelet

Denoising and Mathematics Morphology. The improved wavelet semisoft

threshold method is used to suppress noise of image, where the algorithm of

Bayes threshold is adopted to calculate the value of threshold. A multi-scale and

multi-structural elements morphological edge detection algorithm with entropy

weights is presented and compared with two more popular morphological edge

detection algorithms from three aspects (run times, SNR and precision).

[76] Presented an improved threshold denoising algorithm based on inter-

scale dependency of wavelet. An efficient method based on threshold denoising

algorithm to remove the noise in the image. To the disadvantages of the unified

threshold denoising method, which causes the image fuzzy distortion because of

“over-killed”, by using inter-scale dependency of wavelets coefficients, some

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edge information that “overkilled” by the unified threshold are extracted and

reserved.

[78] Presented a new Adaptive Switching Median (ASWM) filter for

removing impulse noise from corrupted images. The originality of ASWM is that

no apriori Threshold is needed as in the case of a classical Switching Median

filter. Instead, Threshold is computed locally from image pixels intensity values

in a sliding window. It provides better performance in terms of PSNR and MAE

than many other median filter variants for random-valued impulse noise.

[79] Presented a median Filter Method for Image Noise Variance

Estimation. Image noise estimation is of crucial importance for the computer

vision algorithm, for the algorithm parameter is always adjusted to account for

the variations in noise level over the captured images. A median filter method is

provided for the image noise variance estimation in the paper. The image was

processed with a group of high pass digital filters constructed by several finite

difference operators with different orders.

[80] Proposed a superior methodology based on improved tolerance

based selective arithmetic mean filtering technique for the detection and removal

of Salt and Pepper Noise on corrupted images is presented in this paper. The

function of the proposed filtering technique is to detect and remove the noisy

pixels and restore the noise free information.

[81] Proposed a new and efficient algorithm for the removal of high

density salt and paper noise and videos. The existing non-linear filter like

Standard Median Filter (SMF), Adaptive Median Filter (AMF), Decision Based

Algorithm (DBA) and Robust Estimation Algorithm (REA) shows better results

at low and medium noise densities. At high noise densities, their performance is

poor. The new algorithm has lower computation time when compared to other

standard algorithms.

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[82] Proposed removal of salt-and-pepper noise based on compressed

sensing. The key point of the method is the sparsity used to reconstruct the

whole image based on partial noise-free pixels. It demonstrates the better

performance of the proposed method compared to the existing modified median-

type filters.

[88] proposed wavelet denoising Double-Threshold Optimization Method

and Its Application. A method based on ant colony algorithm is given for

optimizing wavelet de-noising double-threshold. The optimization interval and

the objective function are chosen according to the difference of autocorrelation

coefficient, which belong to signal’s wavelet coefficient and noise’s wavelet

coefficient respectively. The optimal upper threshold and lower threshold are

calculated by ant colony algorithm.

[98] Presented real-time dynamically reconfigurable 2-D filter banks. It

is based on separable one-dimensional filters. At the lowest level, each 2D filter

is implemented using dynamic reconfiguration between two one-dimensional

filters. Then, at a higher level, filter banks are implemented using dynamic

partial reconfiguration of efficient 1D filter blocks (based on distributed

arithmetic).

[101] Proposed reconfigurable hardware for median filtering for image

processing applications. Median filter is a non-linear filter used in image

processing for impulse noise removal during morphological operations, image

enhancement and other image processing operations. It finds its typical

application in the situations where edges are to be preserved for higher level

operations like segmentation, object recognition etc. Real-time applications, such

as video and high speed acquisition cameras often require fast algorithms for

processing.

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[91] Proposed the framework for reconfigurable architecture based

floating point Discrete-Wavelet Transform computation algorithm. The discrete

wavelet transform has taken its place at the forefront of research for the

development of signal and image processing applications. Hence, this work is

proposed on the design of hardware for the computation of Floating Point

Discrete Wavelet Transform using Harr wavelet. The hardware was implemented

using FPGA with gate level.

[93] Proposed image denoising using multi-scale thresholds method in the

wavelet domain. Images often contain noise due to the capturing devices

environment. For further processing, compression, fractal, etc the image

denoising is necessary. Wavelet analysis plays a very important role in image

denoising. It improves the wavelet thresholding method by using multi-scale

thresholds and a new thresholding function and in case of large noise, a median

filter is suggested.

[95] Proposed a modified Retinex Algorithm based on wavelet

transformation. Retinex method mainly consists of two steps: estimation and

normalization of illumination. The illumination is estimated as a smooth version

of input image using low-pass filters. Some high-frequency components of

image will inevitably be lost in the filtering processing, and images will lose

details and information, correspondingly.

[97] Presented switching bilateral filter with a texture/noise detector for

universal noise removal. In this work, for detection, a sorted quadrant median

vector (SQMV) scheme, which includes important features such as edge or

texture information is proposed. This information is utilized to allocate a

reference median from SQMV, which is in turn compared with a current pixel to

classify it as impulse noise, Gaussian noise, or noise-free.

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[108] proposed a new Adaptive Weight Algorithm for Salt and Pepper

Noise Removal that consists of two major steps, (i) to detect noise pixels

according to the correlations between image pixels, (ii) use adaptive methods

based on the various noise levels. For the low noise level, neighbourhood signal

pixels mean method is adopted to remove the noise and for the high noise level,

an adaptive weight algorithm is used.

[106] proposed a comprehensive Analysis and Parallelization of an

Image Retrieval Algorithm. The advent of multi-core hardware has opened new

opportunities to improve the effectiveness of multimedia data processing. It

make a comprehensive analysis on different potential parallelism, including

pipeline parallelism, task parallelism at both scale level and block level, data

parallelism, and their combinations, in a typical image retrieval algorithm called

SURF, which is the core algorithm of many multimedia (i.e., image and video)

retrieval applications.

[27] Describes the new approach to construct the best tree on the basis of

Shannon entropy. The proposed algorithm provides a good compression

performance. Basis functions are obtained from a single photo type wavelet

called the mother wavelet by dilation (scaling) and translation (shifts). These sets

are divided into four parts such as approximation, horizontal details, vertical

details and diagonal details. They have implemented the proposed algorithm,

wavelet packet best tree using Shannon entropy. In [104], the Wavelet Packet

Best Tree using Shannon entropy has been presented. An extensive result has

been taken on different images.

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2.4 REPORTED WORKS ON CURVELETS, CONTOURLETS AND

2D-PCA

[34] Proposed texture orientation and anisotropy calculation by Fourier

transform and Principal Component Analysis. It proposes a simple method to

calculate a texture angle of orientation and degree of anisotropy. The principle of

the algorithm is to calculate the image Fourier transform modulus and then to

characterize the distribution of this spectrum around the zero frequencies by

Principal Component Analysis. The algorithm returns both the angle of

orientation and an index of confidence.

[84] Presented image denoising using contourlet and two-dimensional

principal component analysis (2DPCA). The noise image was decomposed using

Contourlet by forming directional sub bands. The 2DPCA is then carried out to

estimate the threshold for the image blocks in high frequency sub bands. Thus

the soft thresholding shrinkage can be employed on the Contourlet coefficients

without estimating the noise variance. The denoising algorithm is validated by

numerical study on two images.

[70] Presented a new method based on Curvelets Transform for Image

Denoising. Curvelet transform that combines both Window Shrink and Bayes

Shrink were reported. Though the Wavelet transform can perform job well, it has

a low Resolving rate in high frequency area and it also lacks of the direction in

dealing with images. Curvelet transform have an efficient way of representing

the line and surface property of image.

[96] proposed wavelets, Curvelets and Wave Atoms for Image Denoising.

The images usually bring different kinds of noise in the process of receiving,

coding and transmission. The wavelet transform, Curvelet transform and wave

atom were used for denoising of a image with Gaussian noise. The digital

implementations of three newly developed multi-scale representation systems

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were proposed. The Curvelet transform and wave atom frame are two kinds of

new multi-scale transform after that is based on wavelet transform.

[109] Presented performance evaluation of Curvelet and Wavelet based

Denoising Methods on Brain Computed Tomography Images. It presents the

evaluation of the effect of noise reduction techniques on the brain Computed

Tomography (CT) images. In particular, multiscale geometric denoising methods

based on Curvelet transform are used and compared with wavelet based

methods. It shows that cycle spinning based Curvelet transform method

outperforms the wavelet based methods not only for the suppression of noise but

also for preservation of fine details, edges and allow the use of a low dose brain

CT images.

[67] Presented image enhancement by fusion in contourlet transform. The

image enhancement algorithms work on a single image. Their performance is

limited to the capacity of the sensor by which the image is taken. It provides the

necessary enhancements to composite image approach for enhancing still

images. The approach proposed combines the relevant features of the input

images and produce a composite image which is rich in information content for

human eye.

[65] Presented gray and color image contrast enhancement by the

Curvelet transform. It has one way to solve the problems is to apply image

enhancement original single image. A lot of algorithms developed in this area

and their performance is limited with the performance of the sensors in which the

image is taken. Either due to design or observational constraints a single image

approach usually fails in providing the necessary enhancements.

[66] Proposed multi-sensor image enhancement and fusion for vision

clarity using contourlet transform. It composites image approaches employ pixel

fusion methods that has advantage of pixel fusion is the images used contain

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the original information. Furthermore, the algorithms are rather easy to

implement and time efficient. An important pre-processing step in pixel based

fusion methods is image registration, which ensures that the data at each source

is referring to the same physical structures.

[16] presented although the wavelet transform has been proven to be

powerful in many signal and image processing applications such as compression,

noise removal, image edge enhancement and feature extraction; wavelets are not

optimal in capturing the two-dimensional singularities found in images.

Therefore, several transforms have been proposed for image signals that have

incorporated directionality and multi-resolution and hence, more efficiently

capture edges in natural images.

[17] Presented directional multiscale modeling of images using the

contourlet transform. In the case of the contourlet transform can assume two

different parent child relationships depending on the number of directional

decompositions in the contourlet subbands. If the two successive scales in which

the parent and children lie have the same number of directional decompositions

then the parent and children would lie in the corresponding directional subbands.

2.5 INFERENCE FROM LITERATURE REVIEW

From the literature review surveyed, it is observed that the functional

behaviour of manufactured surfaces is influenced by errors such as roughness,

waviness and form errors that are present on the surface and these errors

influence the functional behaviour. Also, it has been observed that current

evolutionary techniques based filtering schemes has practical limitation when

applied for complex real world problems. The search spaces can become vast for

large circuits and a greater deal of research needs to be directed at scalability. In

this work, it is presented that, one can still evolve circuits with limited

interactions that can be used by traditional designers as building blocks for larger

circuits. Initial research involved evolving circuits at a very high primitive gate

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level and results obtained using this approach showed that evolved circuits were

less useful for more demanding commercial applications. To overcome this

problem a function-level evolution is proposed in this work and domain

knowledge is used to select high level computational units, which can be

represented directly in the chromosome. Previous reported works on machined

image enhancement depends on model based approach as compared to the EHW

based image enhancement filter using coordinate logic operators and functional

level evolution concept presented in this work. Image enhancement schemes

reported so far are dependent on the noise frequency band and machining

specifications. On the contrary, in this work the presented evolutionary operator

and 2D transforms based schemes have the advantage, that it is independent of

the frequency band in which the noise affects the image and specifications of

milling and grinding. In most of the analogous works surveyed and presented,

the use of wavelet transform for designing filter to extract the image features as

well to denoise the image is less suited for image alignment. However, in this

work, it is made suited to detect a highly anisotropic element that includes image

alignments by proposing a 2D-PCA based enhancement scheme. As a result, the

presented feature extraction technique can be adapted generically in machine

vision applications.