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author:

Chen, C.-L. (Chen, C.-L..) [1] | Ye, D.-Y. (Ye, D.-Y..) [2] | Chen, Z.-J. (Chen, Z.-J..) [3]

Indexed by:

Scopus PKU CSCD

Abstract:

A multi-local kernels' fusion algorithm is proposed to solve the problem of high complexity of dark channel priors. In this algorithm, the local kernels are computed and solved in parallel, and then merged into a global kernel (point spread function) by using the shape similarity of local kernels. For the noise that has appeared on the initially merged global kernel, the relevance adjustment is introduced using the neighboring information to further improve the fusion effect. Experiments show that the proposed algorithm can effectively improve the speed of image deblurring while guaranteeing the deblurring effect. It also has better effect on the local detail restoration of some real blurred images, and can handle large-size blurred images well. © 2018, Science Press. All right reserved.

Keyword:

Blind deconvolution; Dark-channel; Image enhancement; Image restoration; Local kernels' fusion; Point spread function; Relevance adjustment

Community:

  • [ 1 ] [Chen, C.-L.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
  • [ 2 ] [Ye, D.-Y.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
  • [ 3 ] [Ye, D.-Y.]Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350116, China
  • [ 4 ] [Ye, D.-Y.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou, 350116, China
  • [ 5 ] [Chen, Z.-J.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China

Reprint 's Address:

  • [Ye, D.-Y.]College of Mathematics and Computer Science, Fuzhou UniversityChina

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Source :

Acta Photonica Sinica

ISSN: 1004-4213

Year: 2018

Issue: 10

Volume: 47

0 . 6 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 4

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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