Project presentation

Salt and Pepper Noise Reducing Median

FilterName: Dipti Jain

Enrolment No.: 9910103508

Introduction

Digital images are often corrupted by Impulse noise due to errors

generated in noisy sensor, errors that occur in the process of converting

signals from analog-to-digital and also errors that are generated in the

communication channels. This error that occurs inevitably alters some of

the pixels intensity while some of the pixels remain unchanged. In order

to remove impulse noise and enhance the affected image quality, the

median filter has been studied and a method based on an improved

median filtering algorithm has been proposed.

Current Problem

Image noise is undesired variation in pixel intensity values in a

captured or transmitted image. Image noise is an unavoidable side-

effect during image capture. It is a phenomenon that no

photographer can ignore. Even if noise is not clearly visible in a

picture, some kind of image noise is bound to exist.

Why is Image Processing Important?

Enhancement – enhances the image, does not increase the information

Compress- minimize the number of bits required to represent the image

Restore- filter the image to minimize the effect of restoration

Why Image De-noising?

Noise maybe due to malfunctioning camera sensors, faulty memory location etc

In transferring, might get distorted. We can still get the data back.

What if the data is already compressed and the information is corrupted due to noising

Hence, we need efficient de-noising algorithms.

Why did I Choose De-Noising?

Nobody likes corrupted images

Images are corrupted very easily and need to be de-noised very often

Also, with the increasing demand of secure data transfer, even a single pixel fault can hamper the message.

Types of Median De-noising

Standard Algorithm

Adaptive Algorithm

Weighted Median

Fuzzy Logic

And many more

Why gray scale?

one channel of color, that normally is necessary just 8 bit to be represented

Because I am learning and new to the concept its better to understand grayscale processing first and then start with color imaging

Faster, simpler

What am I doing?

Comparing the Standard Median De-noising Algorithm and the Noise estimation Based Median De-noising algorithm.

Also, trying to implement an algorithm that would identify the noise and remove from edges and corners as well and is faster and more efficient than the Standard Median De-noising algorithm.

Simple Median Filter

Proposed Algorithm

In the standard median filter, Noise from the edges and corners is not removed. So, instead of replacing only the centre pixel value with the median pixel value, the noise pixel can be identified and replaced.

Median pixel of the window is calculated and stored.

Difference of each pixel with its neighbouring pixels is calculated.

If the difference is either higher or lower than the stored median value, then that pixel is identified as noise and replaced with the median value.

Then the histogram based approach is applied to remove the leftover noise.

Input

Output of fast filter

Output of simple filter