Quantifying the Impact: Evaluating Image Contrast Enhancement Methods in Modern Applications

Abstract

In the realm of digital image processing, enhancing contrast is a fundamental step in improving visual quality and aiding in subsequent analysis tasks. This research paper delves into a comprehensive evaluation of contemporary image contrast enhancement techniques, aiming to provide a nuanced understanding of their efficacy in modern applications. The study scrutinizes six prominent methods, including Histogram Equalization, Probability and Statistics-based Segmentation, DCT-based Compression, Fourier Transforms, Image Restoration and Denoising, and Integration Techniques. Each technique is dissected, highlighting its mathematical foundation, crucial parameters, and experimental setup. The comparative analysis encompasses considerations of computational cost, user-friendliness, versatility, and potential synergies between methods. The findings illuminate the diverse strengths and limitations of each approach, empowering practitioners to make informed choices based on specific image processing requirements. This research contributes a comprehensive framework for quantifying the impact of image contrast enhancement techniques,