Single Image Super-Resolution Using a Deep Encoder-Decoder Symmetrical Network with Iterative Back Projection

TL;DR: In this paper , a hybrid robust convolutional autoencoder (HRCAE) was proposed for unsupervised anomaly detection of machine tools under noises, which can effectively fuse multi-sensor information and enhance network robustness by training in parallel.

TL;DR: Zhang et al. as discussed by the authors proposed a novel generative adversarial network-based mobile image enhancement method, referred to as MIEGAN, which consists of a novel multi-module cascade generative network and a novel adaptive multi-scale discriminative network.

TL;DR: This article proposes a novel kernel attention module (KAM) for SISR, which enables the network to adjust its receptive field size corresponding to various scales of input by dynamically selecting the appropriate kernel.

TL;DR: This work proposes a novel real-time video SR method that improves the gated residual unit (GEU), which can combine input and output signals with trainable weights, and integrate GEU to the network architecture and aligns each reference frame with a pair of 1D motion convolution kernels, predicted from the convolution kernel estimation network.

TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.

TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.

TL;DR: Quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures, including FCN and DeconvNet.

TL;DR: Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.