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Blind image deblurring with reinforced use of edges

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Abstract

Blind image deblurring tries to restore a blurred image to a clear image without the blurring kernel known in advance, which is widely required in applications such as computer vision and medical image processing. With regard to this, the key issues here are to accurately estimate the blurring kernel for deconvolution of a blurred image, and avoid the ringing artifacts in the restored image, which are both related to high-quality detection of edge information in the blurred image. Though much endeavor has been made, it is still difficult to extract edge information well in blurred images and lacks investigation how edge information causes ringing artifacts. In this paper, we make a study on this and develop novel measures to optimize edge extraction and determine suitable width and weights for the extracted edges for reinforcing their use in deblurring, by which image deblurring can be improved with ringing artifacts considerably suppressed. Experimental results demonstrate our improvements over the existing methods.

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Funding

This project was partially supported by the National Natural Science Foundation of China (Grant Nos. 61661146002, 61872347) and Special Plan for the Development of Distinguished Young Scientists of ISCAS (Y8RC535018).

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Authors and Affiliations

  1. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China

    Qiu Feng, Hou Fei & Wang Wencheng

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Qiu Feng, Hou Fei & Wang Wencheng

Authors
  1. Qiu Feng
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  2. Hou Fei
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  3. Wang Wencheng
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Correspondence to Wang Wencheng.

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Feng, Q., Fei, H. & Wencheng, W. Blind image deblurring with reinforced use of edges. Vis Comput 35, 1081–1090 (2019). https://doi.org/10.1007/s00371-019-01697-4

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