Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding

Yongbo Li, Weisheng Dong, Guangming Shi, Xuemei Xie; The IEEE International Conference on Computer Vision (ICCV), 2015, pp. 450-458

Abstract


Existing approaches toward Image super-resolution (SR) is often either data-driven (e.g., based on internet-scale matching and web image retrieval) or model-based (e.g., formulated as an Maximizing a Posterior estimation problem). The former is conceptually simple yet heuristic; while the latter is constrained by the fundamental limit of frequency aliasing. In this paper, we propose to develop a hybrid approach toward SR by combining those two lines of ideas. More specifically, the parameters underlying sparse distributions of desirable HR image patches are learned from a pair of LR image and retrieved HR images. Our hybrid approach can be interpreted as the first attempt of reconciling the difference between parametric and nonparametric models for low-level vision tasks. Experimental results show that the proposed hybrid SR method performs much better than existing state-of-the-art methods in terms of both subjective and objective image qualities.

Related Material


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[bibtex]
@InProceedings{Li_2015_ICCV,
author = {Li, Yongbo and Dong, Weisheng and Shi, Guangming and Xie, Xuemei},
title = {Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}