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Enhanced Deep Residual Networks for Single Image Super-Resolution

Enhanced Deep Residual Networks for Single Image Super-Resolution

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Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an en-hanced deep super-resolution network (EDSR) with perfor-mance exceeding those of current state-of-the-art SR meth-ods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method,which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art meth-ods on benchmark datasets and prove its excellence by win-ning the NTIRE2017 Super-Resolution Challenge [26].
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