Analytical Review of Image Denoising using Independent Component Analysis

Noise removal (denoising) is one of the most fundamental challenges in modern image processing. Various techniques aim to separate noise from the desired image, thereby improving visual quality and feature preservation. Among these, Independent Component Analysis (ICA) has emerged as an effective method, leveraging the non-Gaussian nature of image components to isolate useful structures from noise. ICA is a powerful tool for blind source separation and image decomposition, with applications ranging from denoising to feature extraction. Despite relatively limited literature, its adaptability and statistical robustness make it highly relevant for today’s imaging systems.