TBNet: Stereo Image Super-Resolution with Multi-Scale Attention
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Published:2023-06-21
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Volume:
Page:
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ISSN:0218-1266
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Container-title:Journal of Circuits, Systems and Computers
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language:en
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Short-container-title:J CIRCUIT SYST COMP
Affiliation:
1. State Gride of China, East Inner Mongolia Electric Power Co., Ltd., Information and Communication Branch, No. 11 Ordos East Street, Saihan District, Hohhot City 010010, P. R. China
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.
Publisher
World Scientific Pub Co Pte Ltd
Subject
Electrical and Electronic Engineering,Hardware and Architecture,Electrical and Electronic Engineering,Hardware and Architecture