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Learning a Mixture of Deep Networks for Single Image Super-Resolution
[article]
2017
arXiv
pre-print
Single image super-resolution (SR) is an ill-posed problem which aims to recover high-resolution (HR) images from their low-resolution (LR) observations. The crux of this problem lies in learning the complex mapping between low-resolution patches and the corresponding high-resolution patches. Prior arts have used either a mixture of simple regression models or a single non-linear neural network for this propose. This paper proposes the method of learning a mixture of SR inference modules in a
arXiv:1701.00823v1
fatcat:7syinfyqbfdwlitfnh62tj3xp4