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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
- Source :
- CVPR 2016, CVPR
- Publication Year :
- 2016
- Publisher :
- IEEE, 2016.
-
Abstract
- Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods.<br />CVPR 2016 paper with updated affiliations and supplemental material, fixed typo in equation 4
- Subjects :
- FOS: Computer and information sciences
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Feature extraction
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Machine Learning (stat.ML)
02 engineering and technology
Iterative reconstruction
Convolutional neural network
Convolution
Statistics - Machine Learning
0202 electrical engineering, electronic engineering, information engineering
Computer vision
Image resolution
Pixel
Artificial neural network
business.industry
020207 software engineering
Filter (signal processing)
Feature (computer vision)
Bicubic interpolation
020201 artificial intelligence & image processing
Artificial intelligence
business
Interpolation
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- CVPR 2016, CVPR
- Accession number :
- edsair.doi.dedup.....becbfdbffca78cbb3d403725ca854f89