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This paper introduces a fast blind deconvolution strategy for image deblurring by modifying a recent natural image model, i.e., the total generalized variation (TGV), which aims at reconstructing an image with higher-order smoothness as well as sharp edges. But, when it turns to the blind issue, as demonstrated either empirically or theoretically by a few previous blind deblurring works, natural image...
This paper presents an automatic deblurring approach for motion blur images. The approach explores the prior of the intensity and gradient to estimate the motion blur kernel from single blurred image. In this way, motion blur kernel could be well estimated not only on daytime images, but also on nighttime images. Efficient optimization method was given for the prior-based approach. Besides, a cost-effective...
How to deal with themotion blurred image is a common problem in our daily life. Restoring blurred images is challenging, especially when both the blur kernel and the sharp image are unknown. In this work, we present a new algorithm for removing motion blur from a single image, which incorporates the image decomposition into the image deblurring process. Most of the existing algorithms solving the...
In this paper, we propose a method for removing motion blur and deringing from images, which can be applied to handle both the camera motion blur and the object motion blur. Fully taking the advantage of abundant information of blurred/noisy image pair, we can extract the alpha mattes of same objects in both burred image and noisy image, and then estimate the blur kernel of blurred image using the...
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