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Normalized filter pool for prior modeling of nature images

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Abstract

Markov random field (MRF), as one of special undirected graphs, is widely used in modeling priors of natural images. Targeting to learn better prior models from a given database, we explore the natural image statistics at different scales and build normalized filter pool, a kind of high-order MRF, for prior learning of nature images. The main contribution of the proposed model is that we construct a multi-scale MRF model through constraining the norms of filters in kernel space and integrate all the filtering responses in a unified framework. We formulate both learning and inference as constrained optimization problems and solve them using augmented Lagrange method. The experiment results demonstrate that the normalization of filters at different scales helps to achieve fast convergence in learning stage and obtain superior performance in image restoration, e.g., image denoising and image inpainting.

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Acknowledgments

We would like thank all the reviewers. This work was supported in part by NSFC (No. 61305026).

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Authors and Affiliations

  1. Microsoft Research, Beijing, China

    Yangang Wang

  2. Tsinghua University, Beijing, China

    Jinli Suo & Qionghai Dai

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  1. Yangang Wang
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  2. Jinli Suo
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  3. Qionghai Dai
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Correspondence to Yangang Wang.

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Wang, Y., Suo, J. & Dai, Q. Normalized filter pool for prior modeling of nature images. Machine Vision and Applications 27, 437–446 (2016). https://doi.org/10.1007/s00138-016-0753-y

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