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TBNet: Stereo Image Super-Resolution with Multi-Scale Attention.
- Source :
- Journal of Circuits, Systems & Computers; 12/1/2023, Vol. 32 Issue 18, p1-17, 17p
- Publication Year :
- 2023
-
Abstract
- With 3D products being widely applied, more attention has focused on studying stereo image super-resolution (SR). Current stereo image SR studies mainly aim to improve the performance by the additional information from a pair of low-resolution stereo images. However, it is challenging for stereo image SR to fully exploit self-similarity information from its own image and parallax information between stereo image pairs. In line with these challenges, this paper presents a Two-Branch Network (TBNet) to integrate self-similarity information and parallax information for SR. In the TBnet, a stereo parallax transfer module with an encoder–decoder structure was first proposed to sufficiently transfer multi-scale parallax information and preserve the stereo consistency between stereo images. This paper further presented a residual pyramid self-attention module to employ self-similarity information to take advantage of self-predictive power. Finally, extensive experiments demonstrate the superiority of our model over the state-of-the-art performance in terms of objective and perceptual quality and the accuracy of disparity estimation. [ABSTRACT FROM AUTHOR]
- Subjects :
- STEREO image
HIGH resolution imaging
PARALLAX
IMAGE denoising
Subjects
Details
- Language :
- English
- ISSN :
- 02181266
- Volume :
- 32
- Issue :
- 18
- Database :
- Complementary Index
- Journal :
- Journal of Circuits, Systems & Computers
- Publication Type :
- Academic Journal
- Accession number :
- 175237811
- Full Text :
- https://doi.org/10.1142/S021812662350319X