MATLAB Implementation and Comparative Enhancement of Inverse Filtering for Image Restoration

Implementation and comparative enhancement of inverse filtering for image restoration using MATLAB. In the field of image processing, image restoration constitutes a significant research direction. The inverse filtering method serves as one of the fundamental approaches for image restoration, utilizing frequency domain information to recover the original image. MATLAB provides powerful tools for implementing inverse filtering algorithms through functions like fft2 (2D Fast Fourier Transform) and ifft2 (inverse FFT) for frequency domain operations, along with psf2otf for converting point spread functions to optical transfer functions. Image enhancement represents another critical aspect of image processing that improves image quality and detail visibility. This research topic involves comparing restoration results with enhancement techniques such as histogram equalization or Wiener filtering, making it both interesting and challenging for evaluating different image quality improvement strategies. The implementation typically involves designing appropriate inverse filters while addressing noise amplification issues through regularization techniques.