Fast and Accurate Single Image Super-Resolution via Information DistillationNetwork
Abstract
Recently, deep convolutional neural networks (CNNs)
have been demonstrated remarkable progress on single image super-resolution. However, as the depth and width of
the networks increase, CNN-based super-resolution methods have been faced with the challenges of computational
complexity and memory consumption in practice. In order
to solve the above questions, we propose a deep but compact convolutional network to directly reconstruct the high
resolution image from the original low resolution image. In
general, the proposed model consists of three parts, which
are feature extraction block, stacked information distillation
blocks and reconstruction block respectively. By combining
an enhancement unit with a compression unit into a distillation block, the local long and short-path features can be effectively extracted. Specifically, the proposed enhancement
unit mixes together two different types of features and the
compression unit distills more useful information for the sequential blocks. In addition, the proposed network has the
advantage of fast execution due to the comparatively few
numbers of filters per layer and the use of group convolution. Experimental results demonstrate that the proposed
method is superior to the state-of-the-art methods, especially in terms of time performance. Code is available at
https://github.com/Zheng222/IDN-Caffe