Abstract

Non-linear Adaptive Statistics Estimation Filter to remove high density Salt and Pepper noise is presented. The algorithm detects the pixel corrupted by salt and pepper noise and replaces them with a value estimated using proposed algorithm. The algorithm detects the corrupted pixel at the initial stage itself. The performance of proposed algorithm is compared with various filters and has better image quality than the existing filters. The proposed method removes noise effectively even at noise level and preserves the fine details and edges effectively with reduced streaking at higher noise densities. The proposed filter has better image quality then existing Non-linear filters of this type.

Introduction

• Digital Image processing and Concepts.

• Noise and Filters

• Average (Mean) Filter

• Median Filter

• Adaptive Filter

• Weighted Median Filter.

Digital Image Processing

• What Is Image Processing.

•Examples and use of Digital Image Processing.

•Fundamental Steps in Digital Image Processing.

•Components of Image processing Systems

DIP (Continuation)

• Gamma Ray Imaging.

• X-Ray Imaging.

• Imaging Ultra Violent Band.

• Imaging Micro Wave Band.

• Imaging Radio Band.

DIP (Continuation)

• Fundamentals of DIP

DIP (Continuation)

• Components of DIP

Image Sensing and Acquisition

• Acquisition Using Single Sensor.

Image Sensing and Acquisition

• Acquisition Using Line Sensor.

Image Sensing and Acquisition

• Acquisition Using Array of Sensor.

Sampling and Quantization

• Basic Components of Quantization

Quantization

• Quantization.

NoiseNoise Noise is any undesirable signal. Noise is everywhere and thus we have to learn to live with it. Noise gets introduced into the data via any electrical system used for storage, transmission, and/or processing. In addition, nature will always plays a “noisy” trick or two with the data under observation. When encountering an image corrupted with noise you will want to improve its appearance for a specific application. The techniques applied are application oriented. Also the different procedures are related to the types of noise introduces to the image. Some examples of noise are: Gaussian or White, Rayleigh, Shot or Impulse, Periodic, Sinusodial or Coherent, Uncorrelated and Granular

Filters

• Mean or Average Filter.

• Median Filter.

• Weighted Median Filter.

• Adaptive Median Filter.

Mean Filter• The mean filter is a simple sliding-window spatial filter that replaces the

center value in the window with the average (mean) of all the pixel values in the window. The window, or kernel, is usually square but can be any shape.

5 3 6

2 1 9

8 4 7

5 + 3 + 6 + 2 + 1 + 9 + 8 + 4 + 7 = 4545 / 9 = 5

* * *

* 5 *

* * *

Center value (previously 1) is replaced by the mean of all nine values (5).

Median Filter• Median Filter follows the basic prescription. The median filter is normally used

to reduce noise in an image. Some what like the mean filter. However it often does a better job than the mean filter preserving the useful details of the image. This class of filter belongs to the class of edge preserving smoothing filters which are non-linear filter this means that two images A(x) and B(x):

Adaptive Median Filter

Removing impulse noise. Smoothing of other noise. Reduce distortion, like excessive thinning

or thickening of object boundaries

Working of Adaptive Filter

• Notations Used

Algorithm of Adaptive Filter

Requirements and Specifications

• Hardware requirements• PC(Intel Pentium Processor with more than 400 MHz)• 256 and Above RAM• 80GB Hard Disk

• Software Requirements• Programming language: C

• MAT LAB Tool

Flow Chart

conclusion