Impact of Wavelet Transform and Median Filtering on removal of Salt and Pepper Noise in Digital Images

Abstract

Image ac q uisition is a common task in ever y image processing operation. Noise is entered during image ac q uisition from its source and once entered it degrades the image and is difficult to remove. In order to achieve the noise cancellation in an image, non-linear filter works better than linear. This paper presents the joint scheme of Wavelet Transform using iterative noise densit y and Median Filtering to remove Salt and Pepper Noise in Digital Images. The first part of the paper derives the wavelet coefficients with slight increase in noise densit y and in second part these coefficients are further modified b y median filter. The algorithm shows the remarkable improvement over Gaussian noise model and removes most of the nois y part from the image and maintains the visual q ualit y . The level of wavelet decomposition is restricted to three. The renowned indexes Peak Signal to Noise Ratio (PSNR) and Root Mean S q uare Error (RMSE) demonstrate marked improvement of image denoising over Gaussian method.

A Review Paper : Noise Models in Digital Image Processing

“…However the image is not fully corrupted by salt and pepper noise instead of some pixel values are changed in the image. Although in noisy image, there is a possibilities of some neighbours does not changed [13][14].…”

A Comparative Study to Noise Models and Image Restoration Techniques

“…So in a salt and pepper noise, progressively dark pixel values are present in bright region and vice versa [27].…”

Image Denoising using Combination of Median Filtering and Wavelet Transform

“…These classes independently carry information of original signal. Figure 1 and 2 below show the DWT decomposition and reconstruction steps of a 2D image signal for level of 2; Median filter [5] works on medial pixel value of its surrounding neighbours. It preserves the smoothness in a resultant image.…”

“…All Discrete wavelet transform [5,[8][9]] is used to find the approximation and detailed coefficients of a discrete signal. It basically represents the time frequency analysis of discrete signal.…”

“…Image denoising [2][3][4][5] plays a vital role in a wide range of applications such as image registration, image segmentation, and image classification and visual tracking. The image may get degraded by noise from any source during acquisition or transferring through any media that is image may get corrupted from intrinsic or extrinsic source.…”