MATLAB Implementation of Wiener Filter and Inverse Filter

This article presents a comprehensive discussion on MATLAB implementations of Wiener filtering and inverse filtering, specifically designed without calling any built-in image processing library functions. Both Wiener filtering and inverse filtering are fundamental techniques in image processing that enhance image quality, reduce noise, and restore blurred images. We will delve into the underlying principles of these filtering methods and provide detailed implementation insights. Our approach involves developing custom MATLAB programs that demonstrate practical applications of these algorithms. The implementation will cover key aspects including frequency domain transformations using FFT (Fast Fourier Transform), point spread function modeling, noise variance estimation, and regularization parameter selection for Wiener filtering. For inverse filtering, we'll address implementation challenges like handling division by zero in frequency domain operations through techniques such as thresholding. Readers will gain practical knowledge on applying Wiener and inverse filtering to image processing tasks while learning how to create MATLAB programs that operate independently of image processing toolbox functions. The article will include code examples showing pixel-wise operations, matrix manipulations for filter kernel creation, and frequency domain filtering procedures.