Quantitative Analysis of Image Enhancement Algorithms for Diverse Applications

This research paper introduces a comprehensive comparative analysis of prominent image enhancement algorithms, including Histogram Equalization, Adaptive Histogram Equalization, CLAHE, Gamma Correction, and Unsharp Masking. In the realm of digital image processing, image enhancement plays a crucial role invarious applications such as medical imaging, remote sensing, surveillance, and computer vision. Addressing the significance of this research, we present an evaluation of these algorithms using key metrics: Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), Contrast Improvement, and Sharpness Improvement. Our methodology encompasses dataset collection, algorithm implementation in MATLAB, and systematic performance evaluation. The results highlight the unique strengths and trade-offs of each algorithm. Histogram Equalization demonstrates moderate improvement in image quality, while Adaptive Histogram Equalization excels in preserving image details despite introducing some distortion. Contrast Limited Adaptive Histogram Equalization strikes a balance between enhancement and computational efficiency. Gamma Correction proves effective for specific adjustments but may compromise overall image quality. Notably, Unsharp Masking stands out with superior sharpness improvement while maintaining image fidelity. In conclusion, the choice of algorithm should be aligned with specific task requirements and the desired balance between image quality and enhancement goals. Considering these outcomes, Unsharp Masking emerges as a promising choice, demonstrating exceptional performance across multiple metrics. This research provides valuable insights for practitioners and researchers seeking to optimize image enhancement algorithms fordiverse applications.