Analysis of the Entropy-guided Switching Trimmed Mean Deviation-based Anisotropic Diffusion filter

TL;DR

This paper presents a hybrid anisotropic diffusion filter that effectively removes salt-and-pepper noise at high densities, outperforming existing methods through a decision-based switching mechanism and PDE formulation.

Contribution

It introduces a novel switching filter combining decision-based and PDE approaches, optimized for high noise levels with adaptive, efficient processing.

Findings

Outperforms existing filters at very high noise densities

Effective at both low and high noise levels

Adaptive filtering guided by input image metrics

Abstract

This report describes the experimental analysis of a proposed switching filter-anisotropic diffusion hybrid for the filtering of the fixed value (salt and pepper) impulse noise (FVIN). The filter works well at both low and high noise densities though it was specifically designed for high noise density levels. The filter combines the switching mechanism of decision-based filters and the partial differential equation-based formulation to yield a powerful system capable of recovering the image signals at very high noise levels. Experimental results indicate that the filter surpasses other filters, especially at very high noise levels. Additionally, its adaptive nature ensures that the performance is guided by the metrics obtained from the noisy input image. The filter algorithm is of both global and local nature, where the former is chosen to reduce computation time and complexity, while the latter is used for best results.