Analytical Comparison of Noise Reduction Filters for Image Restoration Using SNR Estimation

TL;DR

This paper compares various noise reduction filters for image restoration by estimating their effectiveness through Signal-to-Noise-Ratio (SNR) to determine which filters best recover degraded images.

Contribution

It introduces an analytical approach to compare noise reduction filters based on SNR estimation, emphasizing the impact of different noise types on filter performance.

Findings

Different filters vary in effectiveness depending on noise type

SNR estimation provides a quantitative basis for filter comparison

Certain filters outperform others in specific noise scenarios

Abstract

Noise removal from images is a part of image restoration in which we try to reconstruct or recover an image that has been degraded by using apriori knowledge of the degradation phenomenon. Noises present in images can be of various types with their characteristic Probability Distribution Functions (PDF). Noise removal techniques depend on the kind of noise present in the image rather than on the image itself. This paper explores the effects of applying noise reduction filters having similar properties on noisy images with emphasis on Signal-to-Noise-Ratio (SNR) value estimation for comparing the results.