Journal of Electronic Imaging · 2017 · 48 citations · 20 references
ArtsComputer ScienceComputer VisionMachine LearningMachine VisionDeep LearningImage AnalysisImage EnhancementImage FilteringImage ProcessingImage RestorationColor BalancingConvolutional Neural NetworksDeep Learning-based Image RestorationFiltersImage InpaintingImage Restoration Quality AssessmentImage-to-image TranslationInpainting
We present a method for the automatic restoration of images subjected to the application of photographic filters, such as those made popular by photo-sharing services. The method uses a convolutional neural network (CNN) for the prediction of the coefficients of local polynomial transformations that are applied to the input image. The experiments we conducted on a subset of the Places-205 dataset show that the quality of the restoration performed by our method is clearly superior to that of traditional color balancing and restoration procedures, and to that of recent CNN architectures for image-to-image translation.
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