Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis
Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis
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DOI：
10.1109/cvpr.2012.6247930
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
Shenlong Wang;Lei Zhang;Yan Liang;Q. Pan
中科院分区：
文献类型：
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作者：
Shenlong Wang;Lei Zhang;Yan Liang;Q. Pan
在各种计算机视觉应用中，我们经常需要将一种风格的图像转换为另一种风格，以便更好地可视化、解释和识别;例如，将低分辨率图像上转换为高分辨率图像，将人脸草图转换为照片进行匹配等。本文提出了半耦合字典学习（SCDL）模型来解决此类跨风格图像合成问题。在SCDL下，将同时学习一对字典和一个映射函数。字典对可以很好地刻画两种风格图像的结构域，而映射函数可以揭示两种风格图像的结构域之间的内在联系。在SCDL中，两个字典不会完全耦合，因此可以为映射函数提供很大的灵活性，以便在样式之间进行准确的转换。此外，聚类和图像非局部冗余的引入，以提高SCDL的鲁棒性。将所提出的SCDL模型应用于图像超分辨率和照片素描合成，实验结果验证了该模型在十字风格图像合成中的通用性和有效性。
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.