Image deconvolution appears in many image-related problems. Previous works tried to train neural networks directly on blurry/clean pairs to restore clean images but failed. In this work, we propose a novel neural network, trained end-to-end, pixels-to-pixels, to deblur images from blurry ones. Our key insight is to build multi-scale convolutional neural networks that extract various scale feature maps which is essential for recovering sharp images and removing artifacts. The networks take input image of arbitrary size and produce output within efficient time. We demonstrate that our approach yields better result than the state-of-the-art deconvolution algorithms on a large dataset.
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