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Image Superresolution using Scale-Recurrent Dense Network
In this paper, we propose a scale recurrent SR architecture built upon units containing series of dense connections within a residual block (Residual Dense Blocks (RDBs)) that allow extraction of abundant local features from the image.
Deep Networks for Image and Video Super-Resolution
To enable super-resolution for multiple factors, we propose a scale-recurrent framework which reutilizes the filters learnt for lower scale factors recursively for higher factors.
Mitigating Channel-wise Noise for Single Image Super Resolution
In practice, images can contain different amounts of noise for different color channels, which is not acknowledged by existing super-resolution approaches.
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