Image Restoration Using Wiener Filter

This text presents MATLAB code implementation for image restoration using Wiener filtering. The Wiener filter demonstrates exceptional performance when processing horizontally motion-blurred images contaminated with noise. Through Wiener filtering, image noise can be effectively reduced while restoring image clarity and detail preservation. The Wiener filter operates as a classical image restoration method based on statistical characteristics of images, achieving restoration and enhancement through optimal filtering principles. In MATLAB implementation, the Wiener filter can be efficiently applied using built-in functions like `deconvwnr()` which requires parameters including the degraded image, point spread function (PSF), and noise-to-signal power ratio. Key implementation steps typically involve: 1. Estimating the motion blur kernel (PSF) for horizontal movement 2. Calculating or estimating the noise characteristics 3. Applying the Wiener filter using frequency-domain processing 4. Post-processing for intensity normalization The MATLAB code structure generally consists of image reading, PSF generation, noise parameter specification, and the core Wiener filter function call. This makes Wiener filtering one of the preferred methods for handling noisy, horizontally motion-blurred images due to its computational efficiency and robust performance in MATLAB environments.