Stereo Cross Global Learnable Attention Module for Stereo Image Super-Resolution

Yuanbo Zhou, Yuyang Xue, Wei Deng, Ruofeng Nie, Jiajun Zhang, Jiaqi Pu, Qinquan Gao, Junlin Lan, Tong Tong; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 1416-1425

Abstract

Stereo super-resolution is a technique that utilizes corresponding information from multiple viewpoints to enhance the texture of low-resolution images. In recent years, numerous impressive works have advocated attention mechanisms based on epipolar constraints to boost the performance of stereo super-resolution. However, techniques that exclusively depend on epipolar constraint attention are insufficient to recover realistic and natural textures for heavily corrupted low resolution images. We noticed that global self-similarity features within the image and across the views can proficiently fix the texture details of low-resolution images that are severely damaged. Therefore, in the current paper, we propose a stereo cross global learnable attention module (SCGLAM), aiming to improve the performance of stereo super-resolution. The experimental outcomes show that our approach outperforms others when dealing with heavily damaged low-resolution images. The relevant code is made available on this link as open source.

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[bibtex]
@InProceedings{Zhou_2023_CVPR, author = {Zhou, Yuanbo and Xue, Yuyang and Deng, Wei and Nie, Ruofeng and Zhang, Jiajun and Pu, Jiaqi and Gao, Qinquan and Lan, Junlin and Tong, Tong}, title = {Stereo Cross Global Learnable Attention Module for Stereo Image Super-Resolution}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {1416-1425} }