? Photographic Appearance Enhancement via Detail-based Dictionary Learning
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (3) :417-429    DOI: 10.1007/s11390-017-1733-z
Special Issue on Software Engineering for High-Confidence Systems Current Issue | Archive | Adv Search << Previous Articles | Next Articles >>
Photographic Appearance Enhancement via Detail-based Dictionary Learning
Zhi-Feng Xie1,2, Shi Tang1, Dong-Jin Huang1,2, You-Dong Ding1,2, Member, CCF, Li-Zhuang Ma2,3, Member, CCF
1. Department of Film and Television Engineering, Shanghai University, Shanghai 200072, China;
2. Shanghai Engineering Research Center of Motion Picture Special Effects, Shanghai 200072, China;
3. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

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Abstract A number of edge-aware filters can efficiently boost the appearance of an image by detail decomposition and enhancement. However, they often fail to produce photographic enhanced appearance due to some visible artifacts, especially noise, halos and unnatural contrast. The essential reason is that the guidance and constraint of high-quality appearance aren't enough sufficient in the process of enhancement. Thus our idea is to train a detail dictionary from a lot of high-quality patches in order to constrain and control the entire appearance enhancement. In this paper, we propose a novel learning-based enhancement method for photographic appearance, which includes two main stages: dictionary training and sparse reconstruction. In the training stage, we construct a training set of detail patches by extracting from some high-quality photos, and then train an overcomplete detail dictionary by iteratively minimizing an L1-norm energy function. In the reconstruction stage, we employ the trained dictionary to reconstruct the boosted detail layer, and further formalize a gradient-guided optimization function to improve the local coherence between patches. Moreover, we propose two evaluation metrics to measure the performance of appearance enhancement. The final experimental results have demonstrated the effectiveness of our learning-based enhancement method.
Articles by authors
Zhi-Feng Xie
Shi Tang
Dong-Jin Huang
You-Dong Ding
Li-Zhuang Ma
Keywordsimage enhancement   dictionary learning   edge-aware filter     
Received 2016-12-09;
Fund:

This work was supported by the National Natural Science Foundation of China under Grant Nos.61303093,61402278,and 61472245,the Innovation Program of the Science and Technology Commission of Shanghai Municipality of China under Grant No.16511101300,the Natural Science Foundation of Shanghai under Grant No.14ZR1415800,and the Gaofeng Film Discipline Grant of Shanghai Municipal Education Commission.

Corresponding Authors: 10.1007/s11390-017-1733-z   
About author: Zhi-Feng Xie received his Ph.D. degree in computer application technology from Shanghai Jiao Tong University, Shanghai, in 2013. He was a research assistant at the Department of Computer Science, City University of Hong Kong, Hong Kong, in 2011. He is now an assistant professor with Shanghai University, Shanghai. His research interests include image/video editing, computer graphics, and digital media technology.
Cite this article:   
Zhi-Feng Xie, Shi Tang, Dong-Jin Huang, You-Dong Ding, Li-Zhuang Ma.Photographic Appearance Enhancement via Detail-based Dictionary Learning[J]  Journal of Computer Science and Technology, 2017,V32(3): 417-429
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