Full Text:   <455>

Summary:  <119>

CLC number: TP391.4

On-line Access: 2019-03-11

Received: 2017-02-21

Revision Accepted: 2017-06-04

Crosschecked: 2019-01-22

Cited: 0

Clicked: 968

Citations:  Bibtex RefMan EndNote GB/T7714


Jian Zhang


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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.2 P.206-221


Automatic image enhancement by learning adaptive patch selection

Author(s):  Na Li, Jian Zhang

Affiliation(s):  School of Science and Technology, Zhejiang International Studies University, Hangzhou 310012, China

Corresponding email(s):   nli@zisu.edu.cn, jeyzhang@outlook.com

Key Words:  Image enhancement, Contrast enhancement, Dark channel, Bright channel, Adaptive patch based processing

Na Li, Jian Zhang. Automatic image enhancement by learning adaptive patch selection[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 206-221.

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T1 - Automatic image enhancement by learning adaptive patch selection
A1 - Na Li
A1 - Jian Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700125

Today, digital cameras are widely used in taking photos. However, some photos lack detail and need enhancement. Many existing image enhancement algorithms are patch based and the patch size is always fixed throughout the image. Users must tune the patch size to obtain the appropriate enhancement. In this study, we propose an automatic image enhancement method based on adaptive patch selection using both dark and bright channels. The double channels enhance images with various exposure problems. The patch size used for channel extraction is selected automatically by thresholding a contrast feature, which is learned systematically from a set of natural images crawled from the web. Our proposed method can automatically enhance foggy or under-exposed/backlit images without any user interaction. Experimental results demonstrate that our method can provide a significant improvement in existing patch-based image enhancement algorithms.




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