Wiener Filter for Image Deblurring and Noise-Reduced Image Enhancement

Wiener filtering is a computational method designed for image deblurring and restoration, effectively enhancing the clarity of noise-contaminated images. Through sophisticated mathematical computations and frequency-domain analysis, Wiener filtering improves image quality and sharpness by minimizing mean square error between the original and degraded images. The algorithm typically involves estimating the power spectra of the original image and noise, combined with the known degradation function. In practical implementation, key functions like MATLAB's deconvwnr utilize Fourier transforms to apply the Wiener filter in the frequency domain using the formula: G(u,v) = [H*(u,v) / (|H(u,v)|² + K)] * F(u,v), where H represents the blur kernel, K the noise-to-signal ratio, and F the degraded image. Widely applied in medical imaging, autonomous driving systems, and photography, Wiener filtering enables better visualization of image details and information, resulting in more authentic, sharper, and analytically accessible images. Its adaptive noise-handling capability makes it particularly valuable for practical image processing applications where precise restoration is critical.