A basic method for undoing the effects of convolution—which frequently causes blurring because of things like motion, defocus, and atmospheric distortions—is image deconvolution. Rebuilding a cleaner image from a degraded version is the aim of deconvolution, which improves image quality for better analysis and interpretation. Applications for this approach can be found in a variety of domains, such as microscopy, astronomy, and medical imaging, where clear images are essential for activities like diagnosis, star observations, and cellular studies. Conventional deconvolution techniques, including the Richardson–Lucy algorithm and Wiener filtering, provide theoretically sound and computationally efficient solutions, but they frequently suffer from noise and intricate blur patterns. Machine learning (ML) models can learn patterns through training on large datasets to improve deblurring performance. Even with these advancements, problems like blur, noise, and computing complexity still exist. Combining conventional techniques with ML and DL algorithms presents exciting prospects for improved image restoration, allowing more accurate and dependable deconvolution for a variety of uses.