Abstract
The accurate kernel estimation is key to the blind motion deblurring. Many previous methods depend on the image regularization to recover strong edges in the observed image for kernel estimation. However, the estimated kernel will be degraded when recovered strong edges are less accurate, especially in images full of small-scale edges. Different from previous methods, we focus on the kernel regularization. Inspired by the fact that the blur kernel is highly related to the continuous camera motion trajectory during the image capturing, we propose to encourage the continuity of the kernel through a kernel prior. The proposed prior measures the continuity of each element in the kernel and generates a continuity map. By encouraging the sparsity of the map using L 0 norm, discontinuous kernel elements are suppressed. Since the model with the proposed prior is non-convex and non-linear, an approximation method is proposed to minimize the cost function efficiently. Numerous experimental results show that our method outperforms state-of-the-art methods on both the normal and challenging cases. Moreover, the proposed prior is able to further improve the performance of existing MAP-based methods.
Highlights
Motion blur is an image degradation caused by the motion between the camera and the scene during the exposure
2 2 is the likelihood term that enforces the similarity between the blurred image y and the latent image degradation k ∗ x. φ(x) is the image prior and ρ(k) is the blur kernel prior
As this paper focuses on kernel estimation, we use existing non-blind deconvolution method in [4] to recover our deblurred image
Summary
Motion blur is an image degradation caused by the motion between the camera and the scene during the exposure. The maximum a posterior (MAP) framework [1]–[5] is commonly used in conventional deblurring methods This framework introduces extra information by the way of priors: min x,k y−k∗x. 2 2 is the likelihood term that enforces the similarity between the blurred image y and the latent image degradation k ∗ x. Enforce the kernel continuity, i.e.the adaptive threshold [1] and the noise pruning [3], [4] These two-step methods lack of a unified cost function, leading to the difficulties on the convergency and the global optimization. We propose a new continuity measurement for the kernel prior in a unified model. A unified model with the kernel continuity prior is proposed.
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