Impulse Noise Removal in Digital Images

Abstract

This paper introduces the concept of image fusion of filtered noisy images for impulse noise reduction. Image fusion is the process of combining two or more images into a single image while retaining the important features of each image. Multiple image fusion is an important technique used in military, remote sensing and medical applications. Five different filtering algorithms are used individually for filtering the image captured from the sensor. The filtered images are fused to obtain a high quality image compared to individually denoised images. In-order to better appraise the noise cancellation behavior of our fusion technique from the point of view of human perception, an edge detection is performed using canny filter for the fused image. Experimental results show that this method is capable of producing better results compared to individually denoised images.

Removal of high density salt-and-pepper noise by recursive enhanced median filtering

“…Median of any sorted sequence consisting of values is given in equation 1. However, many improvements [4][5][6][7][8] in median filters have been suggested for noise filtering of image and thus they were tend to modify both noisy and noise-free pixels. These filters effectively remove impulse noise but at the cost of blurring into the image and distorted features [5].…”

Advance Impulse Noise Filtering using Double Threshold with Edge Preservation

“…[5] Impulse noise is completely independent and not correlated to the pixels and is randomly distributed over the entire image. Hence unlike Gaussian noise, for an corrupted image effected by impulse noise, all the image pixels are not noisy, only a number of image pixels are effected by impulse noise and the rest of pixels will be noise free.…”

“…Median filter also knows as simple median filter (SMF) [5]. It is very common used non-linear filter.…”

Performance Comparison of Various Image Denoising Filters under Spatial Domain

“…In salt and pepper type of noise, the noisy pixels takes either salt value (gray level -225) or pepper value (grey level -0) and it appears as black and white spots on the images In case of random valued impulse noise, noise can take any gray level value from zero to 225. In this case also noise is randomly distributed over the entire image and probability of occurrence of any gray level value as noise will be same [5]. Reasons for Salt and Pepper Noise: 1) Due to failure of memory cells or wrong working of sensor cells of camera.…”