Image Super-Resolution Using Deep Convolutional Networks

Super resolution convolutional neural networks was introduced as a new deep learning method for single image super-resolution compared to traditional methods such as sparse coding. They also show that conventional sparse-coding-based SR methods can be reformulated into a deep convolutional neural network. The proposed model learns an end-to-end mapping between low and high resolution images, with little extra pre/post-processing beyond the optimization. With a lightweight structure, the SRCNN achieved superior performance than the other methods at the time.

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