Cascading and Enhanced Residual Networks for Accurate Single-Image Super-Resolution

Abstract

Deep convolutional neural networks (CNNs) have contributed to the significant progress of the single-image super-resolution (SISR) field. However, the majority of existing CNN-based models maintain high performance with massive parameters and exceedingly deeper structures. Moreover, several algorithms essentially have underused the low-level features, thus causing relatively low performance. In this article, we address these problems by exploring two strategies based on novel local wider residual blocks (LWRBs) to effectively extract the image features for SISR. We propose a cascading residual network (CRN) that contains several locally sharing groups (LSGs), in which the cascading mechanism not only promotes the propagation of features and the gradient but also eases the model training. Besides, we present another enhanced residual network (ERN) for image resolution enhancement. ERN employs a dual global pathway structure that incorporates nonlocal operations to catch long-distance spatial features from the the original low-resolution (LR) input. To obtain the feature representation of the input at different scales, we further introduce a multiscale block (MSB) to directly detect low-level features from the LR image. The experimental results on four benchmark datasets have demonstrated that our models outperform most of the advanced methods while still retaining a reasonable number of parameters.

Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review

“…Environment, flora, fauna, handmade objects, people, and scenery are the key items found in the dataset [36]. Enhanced deep residual network (EDSR) [37], multi-connected convolutional network for super-resolution (MCSR) [38], cascading residual network (CRN) [39], enhanced residual network (ERN) [39], residual dense network (RDN) [40], Dilated-RDN [35], dense space attention network (DSAN) [41] and dual-branch convolutional neural network (DBCN) [42] were the algorithms that used the DIV2K dataset as their training dataset.…”

“…Another type of loss is the mean absolute error (MAE), also known as L 1 loss. Although L 1 loss may not help the model in achieving a better PSNR as compared to L 2 loss, L 1 loss provides a powerful accuracy and convergence ability to the model [39]. EDSR, CRN, ERN, DRCN, RDN, Dilated-RDN, DSAN, DBCN, and SICNN were using MAE as learning strategies during the model training.…”

Named entity recognition for Chinese judgment documents based on BiLSTM and CRF

“…More recent work on NER is usually based on machine learning methods, in particular, deep learning methods [15,16]. It is also extended to many other languages than European languages.…”

SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

“…Face detection is a major issue in target detection. Many scholars have made significant progress in related fields [27][28][29]. For faces of different sizes, Guo et al [30] proposed MSFD, which is a multi-scale face detector in the reception domain and can detect faces of different scales.…”