A Performance Study of Image Quality Attributes on Smoothened Image Obtained by Anisotropic Diffusion-Based Models

Abstract

Image acquisition systems usually acquire images with distortions due to various factors associated with digitization processes. Poisson is one of the common types of noises present in the image, and it distorts the fine features. Hence, it is necessary to denoise the noisy image by smoothing it to extract the features with fine details. Among the denoising methods, anisotropic diffusion method provides more adequate results. In this chapter, the authors dealt with existing models such as Perona-Malik (PM), total variation, Tsai, Chao, Chao TFT, difference eigen value PM, adaptive PM, modified PM, and Maiseli models. The performances of the models were tested on synthetic image added with the Poisson noise. Quality metrics are used to quantify and to ensure the smoothness of the resultant images. However, in order to ensure the completeness of the denoising effect, the qualitative attributes such as sharpness, blurriness, blockiness, edge quality, and false contouring are considered on smoothened images. The analysis results are shown the completeness of the denoising effect of the models.