SRNSSI: A Deep Light-Weight Network for Single Image Super Resolution Using Spatial and Spectral Information
| Autor: | Alireza Esmaeilzehi, M. Omair Ahmad, Mallappa Kumara Swamy |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
Wavelet transform 02 engineering and technology Residual Computer Science Applications Set (abstract data type) Computational Mathematics Feature (computer vision) Signal Processing 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Image resolution Spatial analysis Algorithm Block (data storage) |
| Zdroj: | IEEE Transactions on Computational Imaging. 7:409-421 |
| ISSN: | 2334-0118 2573-0436 |
| DOI: | 10.1109/tci.2021.3070522 |
| Popis: | Design of a residual block that provides a rich set of features while requiring only small numbers of parameters and operations is crucial for the task of single image super resolution. This is especially important in applications with limited power and storage capacity. In this paper, a new multi-domain residual block is proposed in order to generate richer set of features for the task of image super resolution. The proposed residual block consists of two feature generation modules. The first one is a spatial information processing module and the second one is a spectral information processing module. The feature maps obtained by these two feature generation modules are concatenatively fused to obtain block's output. The new residual block is used to build light-weight super resolution networks. Extensive experiments are performed using several benchmark datasets in order to evaluate the performance of the networks using the new multi-domain residual block. It is shown that the use of both the spatial and spectral features enhances the performance of the light-weight super resolution networks. |
| Databáze: | OpenAIRE |
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