Open AccessPosted Content
Deeply-Recursive Convolutional Network for Image Super-Resolution
Reads0
Chats0
TLDR
This work proposes an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN) with two extensions: recursive-supervision and skip-connection, which outperforms previous methods by a large margin.Abstract:
We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Our network has a very deep recursive layer (up to 16 recursions). Increasing recursion depth can improve performance without introducing new parameters for additional convolutions. Albeit advantages, learning a DRCN is very hard with a standard gradient descent method due to exploding/vanishing gradients. To ease the difficulty of training, we propose two extensions: recursive-supervision and skip-connection. Our method outperforms previous methods by a large margin.read more
Citations
More filters
Posted Content
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig,Lucas Theis,Ferenc Huszar,Jose Caballero,Andrew Cunningham,Alejandro Acosta,Andrew Peter Aitken,Alykhan Tejani,Johannes Totz,Zehan Wang,Wenzhe Shi +10 more
TL;DR: SRGAN, a generative adversarial network (GAN) for image super-resolution (SR), is presented, to its knowledge, the first framework capable of inferring photo-realistic natural images for 4x upscaling factors and a perceptual loss function which consists of an adversarial loss and a content loss.
Proceedings ArticleDOI
Residual Dense Network for Image Super-Resolution
TL;DR: This paper proposes residual dense block (RDB) to extract abundant local features via dense connected convolutional layers and uses global feature fusion in RDB to jointly and adaptively learn global hierarchical features in a holistic way.
Proceedings ArticleDOI
NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study
Eirikur Agustsson,Radu Timofte +1 more
TL;DR: It is concluded that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on the authors' newly proposed DIV2K.
Book ChapterDOI
Accelerating the Super-Resolution Convolutional Neural Network
TL;DR: Zhang et al. as mentioned in this paper proposed a compact hourglass-shape CNN structure for faster and better image super-resolution, which can achieve real-time performance on a generic CPU while still maintaining good performance.
Posted Content
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
TL;DR: This work proposes a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections, and proposes a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Journal ArticleDOI
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.