Highlights
Automatically enhance, divide, and validate the COVID-19 CT images into regions with similar properties such as contrast and structure.
Efficient Kapur entropy-based multilevel thresholding unsupervised procedure.
Measure, visualize, and study comparisons of the infected by COVID-19 volume.
The experiment results indicate that the proposed method can reach the desired heat-mapping of COVID-19 lesion and has the potential to be used for clinical application including developing country.
Enhancing facial images captured under different lighting conditions is an important challenge and a crucial component in the Automatic Face Recognition Systems (AFRS). We tackle this problem by proposing a new face image enhancement approach based on Fuzzy theory. Depending on the illumination of a given image, the Fuzzy-logic generates an adaptive factor which is used for correcting the illumination. The proposed approach improves non-uniform illumination and low contrasts, often encountered during capturing process in severe environmental conditions. Our approach is assessed using four blind-reference image quality metrics as well as visual assessment. A comparison to six state-of-the-art methods is provided. Experiments are performed on four public data sets, namely EYale-B, Mobio, FERET and CMU-PIE, showing very interesting results achieved by our approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.