Review on Performance Analysis of PDE Based Anisotropic Diffusion Approaches for Image Enhancement

Abstract

Partial differential equation based anisotropic diffusion techniques are used extensively in computer vision for image enhancement and de-noising. Anisotropic diffusion is found to be an efficient and low computational complexity approach that has overcome the undesirable effects of linear smoothing filters and now is popular in prominent research areas of enhancing the quality of low contrast images and speckle noise reduction from geological, industrial, and medical images. This paper presents state-of-the-art anisotropic diffusion technique and a comprehensive survey on various advancements in anisotropic diffusion for image enhancement and de-noising. The capability of anisotropic diffusion for enhancing the quality of low contrast images and speckle noise reduction from medical and industrial images are further explored. Various quality measures used to validate the performance are studied. The major research issues and possible future scopes in anisotropic diffusion filtering are also discussed.