Blind Deconvolution Restoration Using Maximum Likelihood Estimation

Blind deconvolution restoration using maximum likelihood estimation can achieve enhanced restoration results through multi-frame image constraints. During the blind deconvolution restoration process, we can improve restoration accuracy and stability by applying constraints from multiple image frames. This method leverages the principle of maximum likelihood estimation, estimating both the blur kernel and original image by maximizing the likelihood function to accomplish blind deconvolution restoration. The implementation typically involves constructing a probabilistic model that treats the observed blurred images as generated from latent sharp images convolved with unknown blur kernels. Key algorithmic steps include: initializing blur kernel estimates, alternating between image restoration and kernel update phases, and incorporating multi-frame constraints through joint optimization. By introducing multi-frame image constraints, we can effectively enhance restoration quality, producing clearer and more accurate restoration results through improved statistical reliability and reduced solution ambiguity. The multi-frame approach allows better noise suppression and more robust kernel estimation compared to single-frame methods.