Orientation-Aware Deep Neural Network for Real Image Super-Resolution

Recently, Convolutional Neural Network (CNN) based approaches have achieved impressive single image super-resolution (SISR) performance in terms of accuracy and visual effects. It is noted that most SISR methods assume that the low-resolution (LR) images are obtained through bicubic interpolation down-sampling, thus their performance on real-world LR images is limited. In this paper, we proposed a novel orientation-aware deep neural network (OA-DNN) model, which incorporate a number of orientation feature extraction and channel attention modules (OAMs), to achieve good SR performance on real-world LR images captured by a single-lens reflex (DSLR) camera. Orientation-aware features extracted in different directions are adaptively combined through a channel-wise attention mechanism to generate more distinctive features for high-fidelity recovery of image details. Moreover, we reshape the input image into smaller spatial size but deeper depth via an inverse pixel-shuffle operation to accelerate the training/testing speed without sacrificing restoration accuracy. Extensive experimental results indicate that our OA-DNN model achieves a good balance between accuracy and speed. The extended OA-DNN*+ model further increases PSNR index by 0.18 dB compared with our previously submitted version. Codes will be made public after publication.