Comparative Analysis of Non-Blind Deblurring Methods for Noisy Blurred Images

Image blurring refers to the degradation of an image wherein image's overall sharpness decreases. is caused by several factors. Additionally, during acquisition process, noise may get added image. Such a noisy and blurred can be represented as resulting from convolution original with associated point spread function, along additive noise. However, often contains inadequate information uniquely determine plausible Based on availability information, deblurring methods classified blind non-blind. In non-blind deblurring, some prior known regarding corresponding function The objective this study effectiveness respect identification elimination present in images. study, three methods, namely Wiener deconvolution, Lucy-Richardson regularized deconvolution were comparatively analyzed for images featuring salt-and-pepper Two types effects simulated, motion Gaussian blurring. said applied under two scenarios: direct after denoising through application adaptive median filter. obtained results then compared each scenario best approach