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In this paper we address the problem of predicting information that have been lost in saturated image areas, in order to enable HDR reconstruction from a single exposure. We show that this problem is well-suited for deep learning algorithms, and propose a deep convolutional neural network (CNN) that is specifically designed taking into account the challenges in predicting HDR values. To train the CNN we gather a large dataset of HDR images, which we augment by simulating sensor saturation for a range of cameras. To further boost robustness, we pre-train the CNN on a simulated HDR dataset created from a subset of the MIT Places database. We demonstrate that our approach can reconstruct high-resolution visually convincing HDR results in a wide range of situations, and that it generalizes well to reconstruction of images captured with arbitrary and low-end cameras that use unknown camera response functions and post-processing. Furthermore, we compare to existing methods for HDR expansion, and show high quality results also for image based lighting. Finally, we evaluate the results in a subjective experiment performed on an HDR display. This shows that the reconstructed HDR images are visually convincing, with large improvements as compared to existing methods.<\/jats:p>","DOI":"10.1145\/3130800.3130816","type":"journal-article","created":{"date-parts":[[2017,11,22]],"date-time":"2017-11-22T16:25:08Z","timestamp":1511367908000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":504,"title":["HDR image reconstruction from a single exposure using deep CNNs"],"prefix":"10.1145","volume":"36","author":[{"given":"Gabriel","family":"Eilertsen","sequence":"first","affiliation":[{"name":"Link\u00f6ping University, Sweden"}]},{"given":"Joel","family":"Kronander","sequence":"additional","affiliation":[{"name":"Link\u00f6ping University, Sweden"}]},{"given":"Gyorgy","family":"Denes","sequence":"additional","affiliation":[{"name":"University of Cambridge, UK"}]},{"given":"Rafa\u0142 K.","family":"Mantiuk","sequence":"additional","affiliation":[{"name":"University of Cambridge, UK"}]},{"given":"Jonas","family":"Unger","sequence":"additional","affiliation":[{"name":"Link\u00f6ping University, Sweden"}]}],"member":"320","published-online":{"date-parts":[[2017,11,20]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276425"},{"key":"e_1_2_2_2_1","volume-title":"Evaluating the Performance of Existing Full-Reference Quality Metrics on High Dynamic Range (HDR) Video Content. 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