report for term end

8/7/2019 report for term end

http://slidepdf.com/reader/full/report-for-term-end 1/23

Sarvajanik Education Society

Sarvajanik College of Engineering &

Technology, Surat

Department of Electronics and communication Engineering ,

CERTIFICATE

This is to certify that the Midterm Project reportentitled Fog Degraded Image Analysis.

is prepared & presented by

Patel Jesis J 60Patel Bhavesh. 61Surati Gaurav 63Suthar Mrunal 45

of B.E. IV Sem VII Electronics & communication Engineering departmentduring year 2010-11. Their work is satisfactory.

Signature of guide Head of the Department



ABSTRACT

Images of outdoor scenes captured in bad weather suffer from poor contrast. Under bad weather conditions, the light reaching a camera is severely scattered by the atmosphere. The resulting decay incontrast varies across the scene and is exponential in the depths of scene points. Therefore, traditional

space invariant image processing techniques are not sufficient to remove weather effects from images.In this paper, we present a physics-based model that describes the appearances of scenes in uniform badweather conditions. Changes in intensities of scene points under different weather conditions providesimple constraints to detect depth discontinuities in the scene and also to compute scene structure. Then,a fast algorithm to restore scene contrast is presented. In contrast to previous techniques, our weather removal algorithm does not require any a priori scene structure, distributions of scene reflectances, or detailed knowledge about the particular weather condition . The method described in this report iseffective under a wide range of weather conditions including haze, mist, fog, and conditions arising dueto other aerosols.[5]

In this report we presents an image enhancement algorithm for fog degraded images. The

proposed method does not require any environmental condition, in which image is captured, to beknown or multiple images, it requires only single input image for the enhancement of its visibility. Alsothe proposed method is computationaly faster as it applies the contrast stretching only of the affected areas of the image, instead of the whole image.



INDEX

Sr.No TITLE PAGE NO.1. 1.1 AIM OF PROJECT

1.2 INTRODUCTION1.2.1 ATMOSPHERIC MODEL1.2.2 CONTRAST ENHANCEMETNT



CHAPTER: 01INTRODUCTION TO IMAGEPROCESSING FOR FOG DEGRADEDIMAGES



1.INTRODUCTION

1.1. Aim of the project- To improve the visibility of degraded image nder bad

weather condition like fog, haze & mist.

1.2. INTRODUCTION

The Degradation of images by fog and mist is a familiar problem. In the literature on atmosphericpropagation, distributions of particles such as fog, mist, cloud, and haze are collectively known asatmospheric aerosols . The effect of such aerosols is to progressively reduce image contrast with

increasing distance. This is due to the following two scattering processes: 1) light reflected from theobject surface is attenuated due to scattering by aerosol particles; and 2) some direct light flux isscattered toward the camera. These effects result in a loss of contrast that is characteristic of poor visibility conditions. The level of contrast reduction increases with the distance from the camera to theobject.

Images taken under bad weather conditions suffer from degradation and severe contrast loss. The degreeof degradation increases exponentially with the distance of scene points from the sensor. The standardfiltering methods cannot restore images degraded by bad weather conditions like fog, mist, haze etc.,hence contrast enhancement methods are used. There can be two approaches for improving the visibilityof fog degraded images. One is based on the atmospheric model and the other is based on the contrast

enhancement.

Many algorithms are available for enhancing image contrast. Of these algorithms, perhaps the bestknown is histogram equalization. However, these algorithms are designed for images that are stationaryin the sense that the image properties are roughly constant across the image. One of the maincharacteristics of atmospheric aerosol degradation is that the local image contrast depends strongly ondistance. Histogram equalization and its variants may be applied locally but low spatial frequencies arethen lost. It seems reasonable to suppose that better results could be obtained by exploiting knowledgeof the characteristics of aerosol degradation.

A considerable amount of research has been directed toward greater understanding of image propagation

through the atmosphere, and this has resulted in the design of sophisticated forward imaging models.However, most of the effort has been in predicting the performance of imaging systems, establishingoptimal imaging wavelengths for particular meteorological and geographical conditions , and inestimating levels of aerosol pollution, and not in image processing. One exception to this is the work of Tajbakhsh and Boyce who propose a model-based method of compensating for the attenuation of terrain-reflected light due to scattering when imaging from an airborne camera. They suggest that arange-dependent scaling should be applied to reverse the Mie scattering effect, and so maintain auniform terrain signal across the image. However, they offer no method for determining the appropriatevalue of the extinction coefficient; this is assumed to be known in advance. More seriously, their imageprocessing procedure takes no account of the direct flux that is scattered toward the camera. This meansthat their algorithm will inevitably fail



in daylight viewing conditions when the flux due to scattered light may be several orders of magnitudegreater than the flux reflected from the terrain.

1.2.1 Atmospheric Model

These methods use physical models to predict the pattern of image degradation and then restore imagecontrast with appropriate compensations. They provide better image rendition but usually require extrainformation about the imaging system or the imaging environment. Oakley used a physics-based methodto restore scene contrast without any predicted weather information by approximating the distribution of radiances in the scene by a single gaussian with known variance, however, in most of atmospheric basedmodels , scene depth need to be estimated beforehand, but it requires more information about the sceneenvironment, like multiple degraded images taken from the same point or both the clear day and foggyday images. Narasimhan and Nayar use two or more different bad weather images taken from the samepoint of view to restore scene structure and contrast based on atmospheric scattering model by assumingthe atmospheric scattering properties invariably. Narasimhan, presented an interactive scene depthestimate method, in which the image contrast can be restored using a single image when the biggest and

smallest scene depth is assigned beforehand.All methods based on physical model either need scene depth information to be known beforehand or multiple degraded images taken from the same point, these requirements make this approach impracticalin some cases.[1]

Fig. 1. Attenuation of light by scattering



1.2.2 .Contrast Enhancement

The most commonly used contrast enhancement method is histogram equalization and its variations.Other image enhancement techniques, that uses histogram equalization are also presented, e.g. in order

to restore contrast of fog-degraded images, which do not need any information regarding the scene depthand avoids complicated atmospheric scattering model.

Histogram equalization is one of the popular contrast enhancement algorithm, due to its simplicity andeffectiveness. However, since it uses histogram information over the whole image, as its transformationfunction, in order to stretch contrast, so it may not reflect scene depth change in different parts of theimage.

Thus, the enhancement effect may not be satisfying when depth changes in the scene. As an illustration,Figure 2 below shows the result of histogram equlization when applied on Figure 1 which shows thatvisibility after histrogram equilization may not improve or there can be added noise which degrades the

visibilty. Thus, defining which areas of the image have to be enhanced becomes important.

Figure 3 shows the results of the proposed method, that has significant improvement in the visiblity ascompared to Figure 2.In this paper, we present an image enhancement algorithm which finds out whichareas of the image are affected by poor contrast, and then enhances the contrast of only those regions.[1]

Figure 1.2.2.1 : Fog- Degraded Image Figure1.2.2.2: Histogram Equalized Image of Figure 1



Figure1.2.2.3: Enhancement of Figure 1 by the proposed



CHAPTER: 02LITERATURE SURVEY



Literature survey

(1) AN IMPROVED FOG-DEGRADED IMAGE ENHANCEMENTALGORITHM

In fog weather, Images captured by outdoor surveillance system degrade

significantly and suffer from poor contrast. This paper presents an improvedalgorithm for enhancing fog-degraded image contrast in which a moving maskis used. By assuming the pixels in a mask having same scene depth, thealgorithm applies the modified partially overlapped sub-block histogramequalization to implement contrast enhancement in every mask.

It involves two steps: first, sky region is segmented in order to restrain over-enhancement in the flat region and reduce noise; then, remove the sky pixels inmasks and modify the histogram information in mask, thus the modifiedpartially overlapped histogram equalization transformation function forenhancement can be gotten.

This paper also proposes anovel fuzzy edge detection algorithm toevaluate above contrast enhancement effect. Experiments on many fog-

degraded images demonstrate that the proposed improved algorithm issimple and effective in contrast enhancement of fog-degraded images.



(2) Image Denoising Based on Undecimated Discrete Wavelet Transform

A new image denoising approach based on undecimated discrete wavelet transform (UDWT) isproposed. The proposed method combines a technique of cone of influence (COI) analyzing andUDWT. Therefore itcan effectively remove the impulse noise and preserve theimage edges.Furthermore, combining with the traditional wavelet thresholding denoising method, it can be used to

welldecrease more widely type of the noise such as Gaussiannoise, poisson noise and even mixed noise.Simulation resultsshow that the filtering performance of the proposedapproach is very satisfactory.

This method is different from the tradition one based on the Fourier transformation signalanalysis, which separates the signal and the noise in the frequency domain, and then filters the noisewith the linearity invariable-time filter. The basic thought of the wavelet de noisingmethod by thresholding is: To make the signal scope and the noise scope is as far as possibledifferent,although the signal and the noise overlap is in the same place in the wavelet domain,so long as their scope is different, we can use the threshold filter method to remove the influence of noise.



(3) Enhancement of Image Degraded by Fog Using Cost FunctionBasedon Human Visual Model

In foggy weather conditions, images become degraded due to the presence of airlight that isgenerated by scattering light by fog particles. In this paper, we propose an effective method to correctthe degraded image by subtracting the estimated airlight map from the degraded image. The airlight mapis generated using multiple linear regression, which models the relationship between regional airlightand the coordinates of the image pixels. Airlight can then be estimatedusing a cost function that is based on the human visual model, wherein a human is moreinsensitive to variations of the luminance in bright regions than in dark regions . For thisobjective, the luminance image is employed for airlight estimation. The luminance image is generatedby an appropriate fusion of the R, G, and B components.Representative experiments on real foggyimages confirm significant enhancement in image quality over the degradedimage.

In this paper, we improve the Oakley method [6] to make it applicable even when the airlightdistribution is not uniform over the image. In order to estimate the airlight, a costfunction that is based on the human visual model is used in the luminance image. The luminance imagecan be estimated by an appropriate fusion of the R, G, and B components. Also,the airlight map is estimated using least squares fitting, which models the relationship between regionalairlight and the coordinates of the image pixels.



(4)Improving Image Quality in Poor Visibility Conditions Using aPhysical Model for Contrast Degradation

In daylight viewing conditions, image contrast is ften significantly degraded by atmospheric aerosolssuch as haze and fog. This paper introduces a method for reducing this degradation in situations inwhich the scene geometry is known. Contrast is lost because light is scattered toward the sensor by theaerosol particles and because the light reflectedby the terrain is attenuated by the aerosol. Thisdegradation is approximately characterized by a simple, physically based model with three parameters.

The method involves two steps: first, aninverse problem is solved in order to recover the three modelparameters; then, for each pixel, the relative contributions of scattered and reflected flux are estimated.The estimated scattercontribution is simply subtracted from the pixel value and the remainder is scaledto compensate for aerosol attenuation. This paper describes the image processing algorithm andpresentsan analysis of the signal-to-noise ratio (SNR) in the resulting enhanced image.

This analysis shows that the SNR decreases exponentially with range. A temporal filter structure isproposed to solve this problem. Results are presented for two image sequences taken from an airbornecamera in hazy conditions and one sequence in clear conditions. A significant improvement in imagequality is demonstrated when using the contrast enhancement algorithm in conjuction with a temporalfilter.



Existing methodology with references

CONTRAST ENHANCEMENT ALGORITHM FOR FOG-DEGRADEDIMAGES

1. INTRODUCTIONImages taken under bad weather conditions suffer from degradation and severe contrast loss. The degreeof degradation increases exponentially with the distance of scene points from the sensor. The standardfiltering methods cannot restore images degraded by bad weather conditions like fog, mist, haze etc.,

hence contrast enhancement methods are used. There can be two approaches for improving the visibilityof fogdegraded images. One is based on the atmospheric model and the other is based on the contrastenhancement.

1.1 Atmospheric ModelThese methods use physical models to predict the pattern of image degradation and then restore image

contrast with appropriate compensations. They provide better image rendition but usually require extrainformation about the imaging system or the imaging environment.Oakley et al. [1,2] used a physics-based method to restore scene contrast without anypredicted weather information by approximating the distribution of radiances in the scene by asingle gaussian with known variance, however, in most of atmospheric based models , scene depth need

to be estimated beforehand, but it requires more information about the scene environment, like multipledegraded images taken from the same point or both the clear day and foggy day images. Narasimhan andNayar use two or more different bad weather images taken from the same point of view to restore scenestructure and contrast based on atmospheric scattering model by assuming the atmospheric scatteringproperties invariably [3,4].Narasimhan et al. [5], presented an interactive scene depth estimate method, in which theimage contrast can be restored using a single image when the biggest and smallest scene depth isassigned beforehand. All methods based on physical model either need scene depth information to beknown beforehand or multiple degraded images taken from the same point, these requirements make thisapproach impractical in some cases.

1.2 Contrast Enhancement

The most commonly used contrast enhancement method is histogram equalization and its variations.Other image enhancement techniques, that uses histogram equalization are also presented, in order torestore contrast of fog-degraded images, which do not need any information regarding the scene depthand avoids complicated atmospheric scattering model.Histogram equalization is one of the popular contrast enhancement algorithm, due to its simplicity andeffectiveness. However, since it uses histogram information over the whole image, as its transformationfunction, in order to stretch contrast, so it may not reflect scene depth change in different parts of theimage. Thus, the enhancement effect may not be satisfying when depth changes in the scene.



CHAPTER: 03SOFTWARE DESIGN



3.1.Design Specification.

The most commonly used contrast enhancement method is histogram equalization and its variations, inorder to restore contrast of fog-degraded images, which do not need any information regarding the scenedepth and avoids complicated atmospheric scattering model. Histogram equalization is one of thepopular contrast enhancement algorithm, due to its simplicity and effectiveness. However, since it useshistogram information over the whole image, as its transformation function, in order to stretch contrast,so it may not reflect scene depth change in different parts of the image. Thus, the enhancement effectmay not be satisfying when depth changes in the scene.

We have implemented an image enhancement algorithm which finds out which areas of the image areaffected by poor contrast, and then enhances the contrast of only those regions. Images which suffer from bad visibility in different parts of the whole image. Some areas of image have poor contrast due to

fog, whereas some area may not have very poor contrast, since it may not be affected by fog. Hencethese class of images have to be enhanced depending on whether a part is affected by dense fog or not.Here we use, HVS characteristics in order to identify two areas. According to HVS characteristics, if apixel is a nonedge or edge pixel it will define that it whether it is affected by fog or not respectively.Hence complete image is searched for edge and nonedge pixels and local enhancement is made only onthe non edge pixel as they are assumed to be affected by dense fog, whereas edge pixels are leftunaltered. For enhancing nonedge pixels we use local histogram equalization which improves thecontrast of the selected part of the image.



3.2. Flow Diagram



3.4.Required Tools and Alternatives

Matlab 7.0

3.5.Standard Stimulation Result.



CHAPTER: 05CONCLUSION



Conclusion

Fog degraded Images along with the result of histogram equalization applied over the whole image, and

enhanced image after applying the proposed method of contrast enhancement. On observing the

visibility of the enhanced image as compared to the degraded images, we can conclude that the proposed

enhancement method is able to enhance the images, better than the histogram equalization applied

globally over the whole image.



REFERENCES

1] Manoj Alwani,Hitendra Gupta,K.K Sharma ,” CONTRAST ENHANCEMENT ALGORITHMFOR FOG-DEGRADED IMAGES” NCVPRIPG 2010,pp 77-78

[2] YU-FENG LI,”Image Denoising Based on Undecimated Discrete Wavelet Transform”Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition,Beijing, China, 2-4 Nov. 2007

[3] Y. S. Zhai and X. M. Liu, "An improved fog-degraded image enhancement algorithm," Wavelet

Analysis and Pattern Recognition,007. ICWAPR'07. International Conference on, vol. 2, 2007

[4] Dongjun Kim, Changwon Jeon, Bonghyup Kang and Hanseok Ko,”Enhancement of ImageDegraded by Fog Using Cost Function Based on Human Visual Model”Proceedings of IEEE International Conference on Multisensor Fusion and Integration for

Intelligent SystemsSeoul, Korea, August 20 - 22, 2008[5] Srinivasa G. Narasimhan and Shree K. Nayar,” Contrast Restoration of Weather Degraded Images”

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL.25, NO. 6, JUNE 2003



History Sheet:

Sr No Date andTime

Discussion and Resolutions of theMeeting

Signature of guide andstudents

1. 4\8\10 PPT for Introduction of ProjectSmall program related to image toolboxin matlab

2. 11\8\10 Introductory Presentation of project

3. 18\8\10 Algorithm overview and understanding

4. 25\8\10 Implementation of algorithm tillthreshold implementation

5. 8\9\10 Understanding of padding commandLiterature study

6. 15\9\10 Implementation of Padding in program& making of sub blocks in image

7. 22\9\10 Complete literature survey andcompletion of 40% of program

8. 29\9\10 Mid term presentation of odd sem andpresenting the work done.

9. 13\10\10 Further implementation of program

10. 20\10\10 Showed the program completion tillstep 5

report for term end

Documents