Single Image Super-Resolution Using Multi-Scale Deep Encoder-Decoder with Phase Congruency Edge Map Guidance

This paper presents an end-to-end multi-scale deep encoder (convolution) and decoder (deconvolution) network for single image super-resolution (SISR) guided by phase congruency (PC) edge map. Our system starts by a single scale symmetrical encoder-decoder structure for SISR, which is extended to a multi-scale model by integrating wavelet multi-resolution analysis into our network. The new multi-scale deep learning system allows the low resolution (LR) input and its PC edge map to be combined so as to precisely predict the multi-scale super-resolved edge details with the guidance of the high-resolution (HR) PC edge map. In this way, the proposed deep model takes both the reconstruction of image pixels’ intensities and the recovery of multi-scale edge details into consideration under the same framework. We evaluate the proposed model on benchmark datasets of different data scenarios, such as Set14 and BSD100 - natural images, Middlebury and New Tsukuba - depth images. The evaluations based on both PSNR and visual perception reveal that the proposed model is superior to the state-of-the-art methods

Decoupling Induction and Multi-Order Attention Drop-Out Gating Based Joint Motion Deblurring and Image Super-Resolution

Resolution decrease and motion blur are two typical image degradation processes that are usually addressed by deep networks, specifically convolutional neural networks (CNNs). However, since real images are usually obtained through multiple degradations, the vast majority of current CNN methods that employ a single degradation process inevitably need to be improved to account for multiple degradation effects. In this work, motivated by degradation decoupling and multiple-order attention drop-out gating, we propose a joint deep recovery model to efficiently address motion blur and resolution reduction simultaneously. Our degradation decoupling style improves the continence and the efficiency of model construction and training. Moreover, the proposed multi-order attention mechanism comprehensively and hierarchically extracts multiple attention features and fuses them properly by drop-out gating. The proposed approach is evaluated using diverse benchmark datasets including natural and synthetic images. The experimental results show that our proposed method can efficiently complete joint motion blur and image super-resolution (SR)

Decoupling Induction and Multi-Order Attention Drop-Out Gating Based Joint Motion Deblurring and Image Super-Resolution

Resolution decrease and motion blur are two typical image degradation processes that are usually addressed by deep networks, specifically convolutional neural networks (CNNs). However, since real images are usually obtained through multiple degradations, the vast majority of current CNN methods that employ a single degradation process inevitably need to be improved to account for multiple degradation effects. In this work, motivated by degradation decoupling and multiple-order attention drop-out gating, we propose a joint deep recovery model to efficiently address motion blur and resolution reduction simultaneously. Our degradation decoupling style improves the continence and the efficiency of model construction and training. Moreover, the proposed multi-order attention mechanism comprehensively and hierarchically extracts multiple attention features and fuses them properly by drop-out gating. The proposed approach is evaluated using diverse benchmark datasets including natural and synthetic images. The experimental results show that our proposed method can efficiently complete joint motion blur and image super-resolution (SR)