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Semi-Coupled Dictionary Learning with Applications to Image Super-Resolution and Photo-Sketch Synthesis
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@MISC{Wang_semi-coupleddictionary,
author = {Shenlong Wang and Lei Zhang and Yan Liang Quan Pan},
title = {Semi-Coupled Dictionary Learning with Applications to Image Super-Resolution and Photo-Sketch Synthesis},
year = {}
}
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Abstract
In various computer vision applications, often we need to convert an image in one style into another style for better visualization, interpretation and recognition; for examples, up-convert a low resolution image to a high resolution one, and convert a face sketch into a photo for matching, etc. A semi-coupled dictionary learning (SCDL) model is proposed in this paper to solve such cross-style image synthesis problems. Under SCDL, a pair of dictionaries and a mapping function will be simultaneously learned. The dictionary pair can well characterize the structural domains of the two styles of images, while the mapping function can reveal the intrinsic relationship between the two styles’ domains. In SCDL, the two dictionaries will not be fully coupled, and hence much flexibility can be given to the mapping function for an accurate conversion across styles. Moreover, clustering and image nonlocal redundancy are introduced to enhance the robustness of SCDL. The proposed SCDL model is applied to image super-resolution and photo-sketch synthesis, and the experimental results validated its generality and effectiveness in cross-style image synthesis. 1.
Keyphrases
semi-coupled dictionary learning photo-sketch synthesis mapping function face sketch dictionary pair accurate conversion image nonlocal redundancy cross-style image synthesis various computer vision application style domain structural domain much flexibility image super-resolution high resolution low resolution image intrinsic relationship cross-style image synthesis problem experimental result scdl model
