Analysis of the Entropy-guided Switching Trimmed Mean Deviation-based Anisotropic Diffusion filter
This work addresses image denoising for applications like medical imaging or photography, but it is incremental as it builds on existing switching and diffusion methods.
The researchers tackled the problem of filtering fixed value impulse noise in images, especially at high noise densities, by proposing a hybrid filter that combines switching mechanisms and anisotropic diffusion, achieving superior performance compared to other filters, particularly at very high noise levels.
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.