Performance Analysis of Spatial and Transform Filters for Efficient Image Noise Reduction

TL;DR

This paper compares various image denoising algorithms, including filtering and wavelet-based methods, with a focus on bilateral filters, demonstrating their effectiveness through quantitative metrics like PSNR and MSE.

Contribution

The study provides a comparative analysis of existing denoising algorithms and introduces an efficient filtering approach that outperforms others in noise reduction quality.

Findings

Efficient filtering approach yields better denoising performance.

Bilateral filters outperform wavelet-based methods in tested scenarios.

Quantitative metrics confirm improved image quality after denoising.

Abstract

During the acquisition of an image from its source, noise always becomes an integral part of it. Various algorithms have been used in past to denoise the images. Image denoising still has scope for improvement. Visual information transmitted in the form of digital images has become a considerable method of communication in the modern age, but the image obtained after the transmission is often corrupted due to noise. In this paper, we review the existing denoising algorithms such as filtering approach and wavelets based approach and then perform their comparative study with bilateral filters. We use different noise models to describe additive and multiplicative noise in an image. Based on the samples of degraded pixel neighbourhoods as inputs, the output of an efficient filtering approach has shown a better image denoising performance. This yields promising qualitative and quantitative results of the degraded noisy images in terms of Peak Signal to Noise Ratio, Mean Square Error and Universal Quality Identifier.