Objective of Denoising Images Corrupted by Mixed Noise through Algorithm Comparison

To accomplish the objective of removing noise from images corrupted by mixed noise contamination, we conducted a comparative study of six distinct denoising methodologies. Our evaluation framework incorporatedmultiple critical factors including algorithmic precision (measured through metrics like PSNR and SSIM), computational efficiency (execution time analysis), and adaptability to diverse noise distributions (Gaussian, salt-and-pepper, Poisson mixtures). Through systematic benchmarking, we identified the optimal denoising algorithm demonstrating superior noise suppression capabilities while preserving edge details and texture information. Key implementation aspects involved wavelet thresholding techniques for multi-resolution analysis, non-local means filtering for structural similarity exploitation, and deep learning architectures with residual connections for feature learning. This algorithm exhibited robust performance across varied scenarios including medical imaging, satellite imagery, and low-light photography. These findings hold significant implications for image processing domains and provide substantial support for related research and practical applications through open-source implementations available in Python/Matlab with customizable parameter tuning.