Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising.

ABSTRACT: A new approach to the evaluation of digital image denoising algorithms is presented. In the proposed method, the mean absolute error (MAE) is decomposed into three components that reflect the different cases of denoising imperfections. Moreover, aim plots are described, which are designed to be a very clear and intuitive form of presentation of the new decomposed measure. Finally, examples of the application of the decomposed MAE and the aim plots in the evaluation of impulsive noise removal algorithms are presented. The decomposed MAE measure is a hybrid of the image dissimilarity measure and detection performance measures. It provides information about sources of errors such as pixel estimation errors, unnecessary altered pixels, or undetected and uncorrected distorted pixels. It measure

SUBMITTER: Malinski L 

PROVIDER: S-EPMC10304227 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature