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Image Colorization With Deep Convolutional Neural

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and Super-Resolution arXiv:1603.08155v1 [cs.CV] 27 Mar 2016

and Super-Resolution arXiv:1603.08155v1 [cs.CV] 27 Mar 2016

arxiv.org

forward image transformation tasks have been solved by training deep convolu-tional neural networks with per-pixel loss functions. Semantic segmentation methods [3,5,12,13,14,15] produce dense scene labels by running a network in a fully-convolutional manner over an input image, train-ing with a per-pixel classi cation loss.

  Image, Tional, Deep, Neural, Convolutional, Convolu, Deep convolu tional neural

Free-Form Image Inpainting With Gated Convolution

Free-Form Image Inpainting With Gated Convolution

openaccess.thecvf.com

convolutional image inpainting network with both global and local consistency to handle high-resolution images on a variety of datasets [18, 32, 53]. This approach, however, still heavily relies on Poisson image blending with tradi-tional patch-based inpainting results [11]. Yu et al. [49] propose an end-to-end image inpainting model by adopt-

  With, Image, Convolutions, Convolutional, Gated, Inpainting, Image inpainting with gated convolution, Convolutional image

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Image, Deep convolu-tional neural, Convolutional, Image Inpainting With Gated Convolution, Convolutional image