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With respect to a downsampled low resolution image, we model a high resolution image as a combination of two components, a deterministic component and a stochastic component. The deterministic component can be recovered from the low-frequency signals in the downsampled image. The stochastic component, on the other hand, contains the signals that have little correlation with the low resolution image. We adopt two complementary methods for generating these two components. While generative adversarial networks are used for the stochastic component, deterministic component reconstruction is formulated as a regression problem solved using deep neural networks. Since the deterministic component exhibits clearer local orientations, we design novel loss functions tailored for such properties for training the deep regression network. These two methods are first applied to the entire input image to produce two distinct high-resolution images. Afterwards, these two images are fused together using another deep neural network that also performs local statistical rectification, which tries to make the local statistics of the fused image match the same local statistics of the groundtruth image. Quantitative results and a user study indicate that the proposed method outperforms existing state-of-the-art algorithms with a clear margin.<\/jats:p>","DOI":"10.1145\/3272127.3275060","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T19:16:10Z","timestamp":1543432570000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Image super-resolution via deterministic-stochastic synthesis and local statistical rectification"],"prefix":"10.1145","volume":"37","author":[{"given":"Weifeng","family":"Ge","sequence":"first","affiliation":[{"name":"The University of Hong Kong"}]},{"given":"Bingchen","family":"Gong","sequence":"additional","affiliation":[{"name":"The University of Hong Kong"}]},{"given":"Yizhou","family":"Yu","sequence":"additional","affiliation":[{"name":"The University of Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2018,12,4]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution. arXiv preprint arXiv:1612.00085","author":"Ahn Il Jun","year":"2016","unstructured":"Il Jun Ahn and Woo Hyun Nam . 2016. 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