Image De-Noising For Salt and Pepper Noise by Introducing New Enhanced Filter

TL;DR

This paper introduces a new enhanced filter for grayscale image de-noising that effectively removes salt and pepper noise, especially at low and medium densities, outperforming existing filters in PSNR and visual quality.

Contribution

The paper presents a novel filtering method that replaces noisy pixels with a trimmed mean, improving de-noising performance over traditional median-based filters.

Findings

Better PSNR and MSE metrics compared to existing filters

Improved visual perception and reduced blurring

Effective at low and medium noise densities

Abstract

When an image is formed, factors such as lighting (spectra, source, and intensity) and camera characteristics (sensor response, lenses) affect the appearance of the image. Therefore, the prime factor that reduces the quality of the image is noise. It hides the important details and information of images. In order to enhance the qualities of the image, the removal of noises become imperative and that should not at the cost of any loss of image information. Noise removal is one of the pre-processing stages of image processing. In this paper a new method for the enhancement of grayscale images is introduced, when images are corrupted by fixed valued impulse noise (salt and pepper noise). The proposed methodology ensures a better output for the low and medium density of fixed value impulse noise as compared to the other famous filters like Standard Median Filter (SMF), Decision Based Median Filter (DBMF) and Modified Decision Based Median Filter (MDBMF) etc. The main objective of the proposed method was to improve peak signal to noise ratio (PSNR), visual perception and reduction in the blurring of the image. The proposed algorithm replaced the noisy pixel by trimmed mean value. When previous pixel values, 0s, and 255s are present in the particular window and all the pixel values are 0s and 255s then the remaining noisy pixels are replaced by mean value. The gray-scale image of mandrill and Lena were tested via the proposed method. The experimental result shows better peak signal to noise ratio (PSNR), mean square error values with better visual and human perception.