This thesis presents a novel blind image deconvolution algorithm for motion deblurring from a single blurred image. We propose a unified framework for both blur kernel estimation and non-blind image deconvolution by combining the proposed Gradient Attenuation Richardson-Lucy (GARL) algorithm with bilateral filtering (BF). In the blur kernel estimation stage, we show that an initial blur kernel, which is used for starting an alternating kernel refinement process, can be obtained from the blurred image with a quadratic regularization approach. In the non-blind image deconvolution stage, we exploit the information of image gradients and develop the GARL algorithm to alleviate the notorious ringing problem in the RL-based image restoration approach. Furthermore, the loss of image details due to the suppression of the ringing artifacts around the regions with strong edges is recovered with an incremental detail recovery procedure. The proposed framework is simple yet effective compared to previous statistical approaches. Experimental results on various real data sets are given to demonstrate the superior performance of the proposed algorithm over the previous methods.

本論文提出了一個新的單張運動模糊影像還原演算法。我們針對模糊軌跡估測以及非盲影像還原，提出了一個結合梯度衰減Richardson-Lucy演算法(GARL)以及雙向濾波器(BF)的大一統架構。在模糊軌跡估測階段，我們實驗發現可由模糊影像本身以及二次規則最佳化方法獲得一模糊軌跡的初始值，此初始值是用於開始一交替式模糊軌跡重建程序。而在非盲影像還原階段，我們利用了影像梯度資訊以及Richardson-Lucy演算法提出一新的影像還原方法以消除波紋環效應。此外，為了恢復非盲影像還原階段所失去的影像細節，我們更提出一漸進式影像細節恢復程序。我們所提出的新方法跟舊有研究的統計方法相比是簡單且有效，經由數組真實影像之實驗結果的呈現，我們的方法有著超出舊有方法的卓越表現。

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