CNN-Based Medical Image Restoration Using Customized Adaptive Histogram Equalization

This research paper presents the implementation of two image restoration techniques, namely AHE filter and CLAHE filter, along with an Enhancer using Python for medical imaging applications. Aim of paper is to improve the quality of degraded medical images by reducing noise, blur, and other imaging artifacts, leading to enhanced diagnostic quality. We discuss the advantages and limitations of these techniques and present empirical evidence of their effectiveness in restoring medical images (Hai et al. J Vis Commun Image Representation 90, 1047–3203, 2023). Furthermore, we outline the key challenges and open research problems in the field and propose potential future research directions. Finally, we present real-world examples of the application of image restoration techniques in medical imaging, such as X-rays, and discuss the potential impact of these techniques on clinical diagnosis and treatment. The AHE and CLAHE filters were evaluated on a Pneumonia Classification model, resulting in an accuracy increase from 80.93% with the Normal dataset to 86.22% with the AHE and CLAHE applied datasets. The AHE applied dataset had a recall of 90% and precision of 88.19%, while the CLAHE applied dataset had a recall of 90% and precision of 88.19%. These results demonstrate the effectiveness of AHE and CLAHE filters in improving image quality and their potential impact on clinical diagnosis and treatment.