Synthetic aperture imaging using multi-view super-resolution

• Published in: Journal of electronic imaging Vol. 32; no. 3; p. 033007
• Main Authors: Zhang, Jiaqing, Pei, Zhao, Jin, Min, Zhang, Wenwen, Li, Jun
• Format: Journal Article
• Language: English
• Published: Society of Photo-Optical Instrumentation Engineers 01.05.2023; SPIE
• Subjects: Algorithms; Information management; Machine vision; China; super-resolution; occlusion; light fields; camera array
• ISSN: 1017-9909; 1560-229X
• DOI: 10.1117/1.JEI.32.3.033007

The occlusion problem is a major challenge in the field of computer vision. Synthetic aperture imaging (SAI) is often used for surface reconstruction of occluded objects. However, SAI usually relies on high-speed information transmission devices. In addition, a large amount of information in the scene is lost, when handling low-resolution input images. This limitation results in unclear reconstructed regions in the synthetic aperture image and thus hinders the application of SAI in downstream tasks. We propose a multi-view super-resolution SAI method. It aims to generate high-resolution synthetic aperture images using images acquired by few of low-resolution acquisition devices. The main contributions of this paper are: (1) a multi-view super-resolution algorithm is proposed. It can generate clear synthetic aperture images in an array with a limited number of cameras. (2) By exploiting the correlation between views, the proposed algorithm can generate super-resolution synthetic aperture images with more accurate image structure and sharper image edges. (3) A feature extraction module is proposed. It can effectively extract the complementary relationship between pictures from different perspectives. The experimental results show that the proposed method can generate a reconstructed image of the occluded object surface with clear edges and accurate structure. Compared to conventional SAI, our method improves 5.7  %    /  21.1  %   on peak signal-to-noise ratio (PSNR)/structure similarity index measure (SSIM) and 4.4  %    /  9.2  %   on PSNR/SSIM respectively on two datasets compared to other state-of-the-art super-resolution methods.