Back to Search
Start Over
Light-field spatial super-resolution via enhanced spatial-angular separable convolutional network.
- Source :
-
Journal of Electronic Imaging . Sep/Oct2022, Vol. 31 Issue 5, p53030-53030-19. 1p. - Publication Year :
- 2022
-
Abstract
- Light-field images captured by light-field cameras usually suffer from low spatial resolution due to the inherent limited sensor resolution. Light-field spatial super-resolution thus becomes increasingly desirable for subsequent applications. Although continuous progress has been achieved, the existing methods still failed to thoroughly explore the coherence among light-field views. To address this issue, we propose an efficient neural network for light-field spatial super-resolution, in which the spatial and angular information can be fully exploited by repeatedly alternating spatial and angular domain. Specifically, an enhanced spatial-angular separable convolution block is proposed to efficiently exploit the correlation information between sub-aperture images. Moreover, a multi-scale feature extraction block is introduced to extract feature representations at different scales and capture rich texture and semantic information. Experimental results on both synthetic and real-world light-field datasets demonstrate that the proposed method outperforms other state-of-the-art methods with higher peak signal-to-noise ratio (PSNR)/structural similarity (SSIM) values and fewer parameters. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10179909
- Volume :
- 31
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Journal of Electronic Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 159958528
- Full Text :
- https://doi.org/10.1117/1.JEI.31.5.053030