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The blind image deblurring is to find the underlying true image and the blur kernel from a blurred observation. This is a well-known ill-conditional problem in image processing field. To obtain a pleasant deblurred result, additional assumptions and prior knowledge are required. Proposed in this work is a simple and efficient blind image deblurring method which utilizes L1-regularized second-order gradient prior. The inspiration for this work comes from the fact that the absolute values of the second-order gradient elements decrease with motion blur. This change is an essential feature of the motion blur process, and we demonstrate it mathematically in this paper. By enforcing the L1 norm constraint to the term involving second-order gradients and incorporating it into the traditional deblurring framework, an effective optimization scheme is explored. The half-quadratic splitting technique is adopted to handle the non-convex minimum problem. Experimental results illustrate that our algorithm outperforms the state-of-art image deblurring algorithms in both benchmark datasets and ground-truth scenes. Besides, this algorithm is simple since it does not require any heuristic edge selection steps or involves too many nonlinear operators. [ABSTRACT FROM AUTHOR]

• Propose a new image prior FOLMP and present an efficient blind deblurring model. • Kernel similarity constraint is used to control the iteration times. • Our approach can perform better than the state-of-the-art methods on various challenging scenes. Fractional-order calculus is an extension of integer order calculus. In signal processing, fractional-order calculus can non-linearly enhance the low-frequency signal and suppress the high-frequency signal. In this paper, a new fractional-order local minimum pixel prior (FOLMP) is proposed by combining fractional-order calculus with the local minimum pixel prior. The FOLMP of the sharp images includes fewer non-zero pixels than the blur images. A new blur kernel estimation algorithm is proposed by combining L 0 regularized FOLMP with the maximum posterior probability. Furthermore, the kernel similarity is employed to adjust the iteration times to accelerate the computational efficiency. Comparative experiments show that the proposed algorithm can perform better on different types of datasets than the most advanced algorithms. In addition, non-overlapping image patches are adopted to compute the FOLMP , and the kernel similarity is used to suppress excessive iterations. Therefore, the proposed algorithm is several times or even tens of times more efficient than the classical prior-based methods. [ABSTRACT FROM AUTHOR]

This work investigates a simple yet effective image deblurring method that combines salient image edges with structures. Typically, the vast majority of edge-based work focuses on advancing the salient edges information for blind image deblurring while ignoring the refined scale of the image structures. With this in mind, we show how to effectively exploit the image structure to blind image deblurring in this paper, i.e., we adopt a mutually guided image filter to guide the restoration of the image structure while salient edges provide edge information for blur kernel estimation. Thus, a more expressive deblurring model that employs L 2 -regularization for salient edges and image structure, together with the L 0 -regularization for image gradients is proposed. We find that the quality of the recovered kernel is thereby improved, and the deblurring results are more satisfactory. Experimental results show that our method outperforms state-of-the-art methods in terms of benchmark-based datasets and real scenarios, as well as computational efficiency. • We develop a novel structure-based deblurring term to provide guidance information for image structures. • We build a more expressive model with image structure and salient edges. • We incorporate the L 0 -norm of the gradient for edge-preserving and detail removal. • Quantitative and qualitative evaluations show that our method is comparable to state-of-the-art methods. [ABSTRACT FROM AUTHOR]