DocumentCode :
106097
Title :
Efficient Patch-Wise Non-Uniform Deblurring for a Single Image
Author :
Xin Yu ; Feng Xu ; Shunli Zhang ; Li Zhang
Author_Institution :
Dept. of Electron. Eng., Tsinghua Univ., Beijing, China
Volume :
16
Issue :
6
fYear :
2014
fDate :
Oct. 2014
Firstpage :
1510
Lastpage :
1524
Abstract :
In this paper, we address the problem of estimating a latent sharp image from a single spatially variant blurred image. Non-uniform deblurring methods based on projective motion path models formulate the blur as a linear combination of homographic projections of a clear image. But they are computationally expensive and require large memory due to the calculation and storage of a large number of the projections. Patch-wise non-uniform deblurring algorithms have been proposed to estimate each kernel locally by a uniform deblurring algorithm, which does not require to calculate and store the projections. The key issues of these methods are the accuracy of kernel estimation and the identification of erroneous kernels. To perform accurate kernel estimation, we employ the total variation (TV) regularization to recover a latent image, in which the edges are better enhanced and the ringing artifacts are reduced, rather than Tikhonov regularization that previous algorithms adopt. Thus blur kernels can be estimated more accurately from the latent image and estimated in a closed form while previous methods cannot estimate kernels in closed forms. To identify the erroneous kernels, we develop a novel metric, which is able to measure the similarity between the neighboring kernels. After replacing the erroneous kernels with the well-estimated ones, a clear image is obtained. The experiments show that our approach can achieve better results on the real-world blurry images while using less computation and memory.
Keywords :
image restoration; TV regularization; Tikhonov regularization; homographic projection; kernel estimation; kernel identification; latent image recovery; latent sharp image estimation; patch-wise nonuniform deblurring; projective motion path models; ringing artifacts; single image; single spatially variant blurred image; total variation regularization; Cameras; Computational modeling; Estimation; Image edge detection; Kernel; Memory management; TV; Blind deconvolution; non-uniform deblurring; total variation (TV) regularization;
fLanguage :
English
Journal_Title :
Multimedia, IEEE Transactions on
Publisher :
ieee
ISSN :
1520-9210
Type :
jour
DOI :
10.1109/TMM.2014.2321734
Filename :
6810179
Link To Document :
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