在影像清晰的條件底下，我們能從其中獲得大量資訊。但時常由於硬體的設定或人為地因
素，使得拍下照片變得模糊。由於無法清楚得知模糊的原因，因此在重建影像時需要同時估計模糊核。在只有一張模糊照片的情況下，同時重建影像及模糊核是個不適定性問題。雖然已經有許多演算法可以重建出相對清晰的影像，但這些方法裡時常忽略或是不清楚初始模糊核的估計。此篇論文探討利用 MAPk 的方法以廣義的狄拉克核為初始核來估計一個相對較接近真實的模糊核。我們會詳細推導其中的理論，並且應用於真實世界的影像上。

Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. This is an ill-posed problem since both true kernel with latent image and delta kernel with blur image can minimize the energy function. Therefore a good initial kernel that helps avoiding blur image with delta kernel for deblur process plays an essential role. Many research have already developed reasonable algorithms, but among them the initial blur kernel required for the algorithms are often unclear. This paper look into the MAPk algorithm based on Bayesian Inference, while the initial kernel of the process is always a delta kernel. The goal of this paper is to analyze and evaluate the blind image deconvolution method-MAPk both theoretically and experimentally. We derive the method mathematically and implement it on a real world images.

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