IEEE Transactions on Pattern Analysis and Machine Intelligence· 2015Q1
Image Super-Resolution Using Deep Convolutional Networks
- 9,911citations
- Q1SCImago
- 2015year
Short summary
A deep convolutional neural network (CNN) directly learns an end-to-end mapping for single image super-resolution (SR), achieving state-of-the-art restoration quality with fast speed for practical use.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a deep convolutional neural network (CNN) for end-to-end single image super-resolution (SR).
- Jointly optimizes all network layers, unlike traditional methods that handle components separately.
- Achieves state-of-the-art restoration quality with fast processing speeds.
- Extends the network to process three color channels simultaneously for improved reconstruction.
AI-generated from the title and abstract; the full text is not read.
Abstract
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 represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.
The authors' abstract, as published at the source. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science