image deblurring using adaptive filtering

Image deblurring using adaptive filters (LMS, RLS) restores sharpness by dynamically adjusting filter parameters for real-time image enhance

Image Deblurring Using Adaptive Filtering

Image deblurring is a crucial task in image processing, aimed at restoring sharpness in blurred images caused by motion, defocus, or noise. Adaptive filtering techniques, such as Least Mean Squares (LMS), Recursive Least Squares (RLS), and Normalized LMS (NLMS), dynamically adjust filter parameters to enhance image quality. These filters iteratively estimate and reduce blurring effects by adapting to variations in the image characteristics.

In this project, MATLAB is used to implement and compare different adaptive filtering techniques for image deblurring. Performance is evaluated using metrics like PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Squared Error), and SSIM (Structural Similarity Index). Adaptive filters offer a real-time, computationally efficient solution for restoring images while preserving edges and details. The project is highly relevant for computer vision, medical imaging, and photography applications.