Title of article :
Image deblurring with matrix regression and gradient evolution
Author/Authors :
Xiang، نويسنده , , Shiming and Meng، نويسنده , , Gaofeng and Wang، نويسنده , , Ying and Pan، نويسنده , , Chunhong and Zhang، نويسنده , , Changshui، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Pages :
16
From page :
2164
To page :
2179
Abstract :
This paper presents a supervised learning algorithm for image deblurring. The task is addressed into the conceptual framework of matrix regression and gradient evolution. Specifically, given pairs of blurred image patches and their corresponding clear ones, an optimization framework of matrix regression is proposed to learn a matrix mapping. For an image to be deblurred, the learned matrix mapping will be employed to map each of its image patches directly to be a new one with more sharp details. The mapped result is then analyzed in terms of edge profiles, and the image is finally deblurred in way of gradient evolution. The algorithm is fast, and easy to be implemented. Comparative experiments on diverse natural images and the applications to interactive deblurring of real-world out-of-focus images illustrate the validity of our method.
Keywords :
Image deblurring , Matrix regression , Gradient evolution , Supervised learning , Interactive deblurring
Journal title :
PATTERN RECOGNITION
Serial Year :
2012
Journal title :
PATTERN RECOGNITION
Record number :
1734519
Link To Document :
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