MSDBPN: multi-column smoothed dilated convolution based back projection network for stereo image super-resolution

• Published in: Pattern analysis and applications : PAA Vol. 28; no. 2
• Main Authors: Zhou, Zihao, Wang, Yongfang, Lian, Junjie
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2025; Springer Nature B.V
• Subjects: Computer Science; Convolution; Error correction; Image reconstruction; Image resolution; Original Article; Parallax; Pattern Recognition; Smoothed dilation convolution; Super-resolution; Deep back projection; Stereo image processing
• DOI: 10.1007/s10044-025-01433-w
• ISSN: 1433-7541

Fully exploiting the parallax information of stereo images for super-resolution (SR) can obtain remarkable performance. The most challenging issue for stereo image SR is how to capture complementary correlation information between the stereo image pair to accurately guide reconstruction. In this paper, we propose a multi-column smoothed dilated convolution based back projection network (MSDBPN) for stereo SR by explicitly learning and exploiting the parallax information. In particular, we incorporate adaptive weighted multi-column smoothed dilated convolutions to rapidly expand the receptive field while maintaining excellent inter-pixel correlation. Meanwhile, we reweight different column feature with adaptive learnable parameter to distinguish contributions. Furthermore, we employ a deep back projection mechanism to calculate projection error and implement self-correction to guide precise reconstruction. Extensive experiments on benchmark datasets demonstrate that our proposed method outperforms other state-of-the-art approaches on both quantitative and qualitative evaluations.