Removal of Impulse Noise from Colour Image Using Different Variations of Non-Linear Filters

Digital images are affected by a variety of noise and one well-known type is impulsive noise. In order to reduce or eliminate noise, many image-denoising algorithms have been created, with varying benefits and limitations. To deal with impulsive and spurious noise in colour images, this study does a thorough examination with an emphasis on the median filter and its various variants. By means of thorough experimentation, the researchers examine the relative performance of different denoising algorithms using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) as standards. The results of the study demonstrate the usefulness of the Vector Median filter, especially in situations with high-density impulsive noise. The Vector Median filter is particularly effective for real-time image processing applications since it performs better and requires less processing time. Furthermore, the Modified Median filter, with its high PSNR and low MSE values, shows potential as a low-density noise solution. This study offers insightful information about image eliminating techniques aimed at reducing impulsive noise in colour images. The study advances the field of image processing by utilising creative methodologies and performance measurements. This has significance for other sectors that depend on accurate image analysis. The study also sets the basis for next investigations focused on improving and expanding the range of denoising algorithms to handle a greater variety of noise kinds and intensities.

I. Introduction

Many median filter variations that can reduce the impulse noise from grayscale images were studied by Anwar Shah et al. [1]. Standard test image ‘cameraman’ is employed for testing purposes. Statistical measurements like Standard deviation and Entropy are utilized for calculating functionality of the proposed method. Adaptive Median Filter (AMF) and Weighted Median Filter (WMF) and gave good performance with low densities.