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. Recently, due to the strength of deep convolutional neural network (CNN), many CNN-based SR methods try to train a deep network to gain better reconstruction performance. Kim et al. propose a 20-layer CNN model known as VDSR [12], which adopts residual learning and adaptive gradient clipping to ease training difficulty. To control the model parameters, the authors construct a deeply-recursive convolutional network (DRCN) [13] by adopting recursive layer. To mitigate training difficulty, Mao et al. propose a very deep residual encoder-decoder network (RED) [17] , which consists of a series of convolutional and subsequent transposed convolution layers to learn end-to-end mappings from the LR images to the ground truths. Tai et al. propose
In REF , authors propose a deep & compact information distillation network (IDN) for the task of SISR.
Fast and Accurate Single Image Super-Resolution via Information Distillation Network
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science