Abstract
We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19]. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates (104 times higher than SRCNN [6]) enabled by adjustable gradient clipping. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.
Keywords
Computer scienceClipping (morphology)Deep learningArtificial intelligenceImage (mathematics)Convergence (economics)Pattern recognition (psychology)Convolutional neural networkComputer vision
Affiliated Institutions
Related Publications
Accelerating Very Deep Convolutional Networks for Classification and Detection
This paper aims to accelerate the test-time computation of convolutional neural networks (CNNs), especially very deep CNNs [1] that have substantially impacted the computer visi...
Image Super-Resolution Using Deep Convolutional Networks
We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is...
Learning Deconvolution Network for Semantic Segmentation
We propose a novel semantic segmentation algorithm by learning a deep deconvolution network. We learn the network on top of the convolutional layers adopted from VGG 16-layer ne...
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting...
Deep Ordinal Regression Network for Monocular Depth Estimation
Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by explori...
Publication Info
- Year
- 2016
- Type
- article
- Citations
- 7279
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
7279
OpenAlex
Cite This
Jiwon Kim,
Jung Kwon Lee,
Kyoung Mu Lee
(2016).
Accurate Image Super-Resolution Using Very Deep Convolutional Networks.
.
https://doi.org/10.1109/cvpr.2016.182
Identifiers
- DOI
- 10.1109/cvpr.2016.182