  Multi-scale approach has been used for blind image / video deblurring
problems to yield excellent performance for both conventional and recent
deep-learning-based state-of-the-art methods. Bicubic down-sampling is a
typical choice for multi-scale approach to reduce spatial dimension after
filtering with a fixed kernel. However, this fixed kernel may be sub-optimal
since it may destroy important information for reliable deblurring such as
strong edges. We propose convolutional neural network (CNN)-based down-scale
methods for multi-scale deep-learning-based non-uniform single image
deblurring. We argue that our CNN-based down-scaling effectively reduces the
spatial dimension of the original image, while learned kernels with multiple
channels may well-preserve necessary details for deblurring tasks. For each
scale, we adopt to use RCAN (Residual Channel Attention Networks) as a backbone
network to further improve performance. Our proposed method yielded
state-of-the-art performance on GoPro dataset by large margin. Our proposed
method was able to achieve 2.59dB higher PSNR than the current state-of-the-art
method by Tao. Our proposed CNN-based down-scaling was the key factor for this
excellent performance since the performance of our network without it was
decreased by 1.98dB. The same networks trained with GoPro set were also
evaluated on large-scale Su dataset and our proposed method yielded 1.15dB
better PSNR than the Tao's method. Qualitative comparisons on Lai dataset also
confirmed the superior performance of our proposed method over other
state-of-the-art methods.
