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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\u2013decoder 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. <\/jats:p>","DOI":"10.1142\/s021812662350319x","type":"journal-article","created":{"date-parts":[[2023,5,13]],"date-time":"2023-05-13T02:33:29Z","timestamp":1683945209000},"source":"Crossref","is-referenced-by-count":1,"title":["TBNet: Stereo Image Super-Resolution with Multi-Scale Attention"],"prefix":"10.1142","volume":"32","author":[{"given":"Jiyang","family":"Zhu","sequence":"first","affiliation":[{"name":"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. 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