Application of Wiener Filter Theory in Image Processing

Wiener filter theory has extensive applications in image processing. Wiener filtering serves as a fundamental image restoration method that effectively reduces noise and blur in images. The implementation typically involves frequency domain operations where the Wiener filter transfer function is calculated using signal and noise power spectra. Additionally, Wiener filtering techniques can be adapted for edge extraction and image enhancement applications. In my research project, I investigated various applications of Wiener filtering and completed a comprehensive final report. The study includes MATLAB implementations demonstrating key algorithms such as frequency domain filtering using fft2 and ifft2 functions, noise estimation techniques, and parameter optimization for different image conditions. I have also prepared a detailed PowerPoint presentation that explains the theoretical principles of Wiener filtering, including the derivation of the minimum mean-square error solution, and demonstrates practical implementations with code examples and visual results. This report and presentation aim to provide detailed information about Wiener filter applications, complete with algorithmic explanations and implementation considerations for digital image processing.