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Multi-scale approach has been used for blind image / video deblurringproblems to yield excellent performance for both conventional and recentdeep-learning-based state-of-the-art methods. Bicubic down-sampling is atypical choice for multi-scale approach to reduce spatial dimension afterfiltering with a fixed kernel. However, this fixed kernel may be sub-optimalsince it may destroy important information for reliable deblurring such asstrong edges. We propose convolutional neural network (CNN)-based down-scalemethods for multi-scale deep-learning-based non-uniform single imagedeblurring. We argue that our CNN-based down-scaling effectively reduces thespatial dimension of the original image, while learned kernels with multiplechannels may well-preserve necessary details for deblurring tasks. For eachscale, we adopt to use RCAN (Residual Channel Attention Networks) as a backbonenetwork to further improve performance. Our proposed method yieldedstate-of-the-art performance on GoPro dataset by large margin. Our proposedmethod was able to achieve 2.59dB higher PSNR than the current state-of-the-artmethod by Tao. Our proposed CNN-based down-scaling was the key factor for thisexcellent performance since the performance of our network without it wasdecreased by 1.98dB. The same networks trained with GoPro set were alsoevaluated on large-scale Su dataset and our proposed method yielded 1.15dBbetter PSNR than the Tao's method. Qualitative comparisons on Lai dataset alsoconfirmed the superior performance of our proposed method over otherstate-of-the-art methods.

Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networks for Non-Uniform Single Image Deblurring