Major 2 final project demonstration video uploaded by Dipti Jain 9910103508 jiit-128 "SALT AND PEPPER NOISDE REDUCING MEDIAN FILTER"

1. Salt and Pepper Noise
Reducing Median Filter
Name: Dipti Jain
Enrolment No.: 9910103508

2. 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.

3. 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.

4. 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

5. 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.

6. 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.

7. Types of Median De-noising
 Standard Algorithm
 Adaptive Algorithm
 Weighted Median
 Fuzzy Logic
 And many more

8. 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

9. 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.

10. Simple Median Filter

11. 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.

12. Input

13. Output of fast filter

14. Output of simple filter