CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
UniHIST: A Unified Framework for Image Restoration With Marginal Histogram Constraints
Mei, Xing1,2; Dong, Weiming2; Hu, Bao-Gang2; Lyu, Siwei1
2015-06
Conference Name2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Source PublicationIEEE Conference on Computer Vision and Pattern Recognition
Conference Date2015-6
Conference PlaceBoston, MA
AbstractMarginal histograms provide valuable information for various computer vision problems. However, current image restoration methods do not fully exploit the potential of marginal histograms, in particular, their role as ensemble constraints on the marginal statistics of the restored image. In this paper, we introduce a new framework, Uni-HIST, to incorporate marginal histogram constraints into image restoration. The key idea of UniHIST is to minimize the discrepancy between the marginal histograms of the restored image and the reference histograms in pixel or gradient domains using the quadraticWasserstein (W2) distance. TheW2 distance can be computed directly from data without resorting to density estimation. It provides a differentiable metric between marginal histograms and allows easy integration with existing image restoration methods. We demonstrate the effectiveness of UniHIST through denoising of pattern images and non-blind deconvolution of natural images. We show that UniHIST enhances restoration performance and leads to visual and quantitative improvements over existing state-of-the-art methods.
KeywordImage Restoration
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11193
Collection模式识别国家重点实验室_多媒体计算与图形学
Corresponding AuthorMei, Xing
Affiliation1.Computer Science Department, University at Albany, SUNY
2.Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Mei, Xing,Dong, Weiming,Hu, Bao-Gang,et al. UniHIST: A Unified Framework for Image Restoration With Marginal Histogram Constraints[C],2015.
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