Image super-resolution using CNNs for dual-focal camera system

Yiman Hou; Qi Li; Huajun Feng; Zhihai Xu; Yueting Chen

doi:10.1117/12.2617074

15 February 2022 Image super-resolution using CNNs for dual-focal camera system

Yiman Hou, Qi Li, Huajun Feng, Zhihai Xu, Yueting Chen

Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 1216641 (2022) https://doi.org/10.1117/12.2617074
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong

Abstract

Image super-resolution (SR) is the problem of recovering a high-resolution (HR) image from a low-resolution (LR) image of the same scene. It is an ill-posed problem since high frequency details are completely lost in low-resolution images. To overcome this problem, many methods based on deep learning have been proposed. In our dual-focal camera system, we use a beam-splitter to capture images of the same scene at different resolutions. The shorter focal length module produces the wide-view image with the low resolution, and the longer focal length module produces the tele-view image via optical zooming. The long-focal image contains more details than short-focal image so can be used to guide short-focal image to recover high frequency part. However, existing SR approaches ignore the nature of image degradation, which limits the use of these approaches in challenging cases. In this paper, we propose a novel method which is based on the dual-focal camera system. To reconstruct a wide-view image with the high-resolution, we design an end-to-end deep learning model. First, we using a blur kernel estimation convolution neural networks (CNNs) to obtain the mapping relationship between the short-focal image and the long-focal image. The kernel estimation network is a Laplacian pyramid, which can efficiently learn per-pixel kernels to recover the HR image. Then, we interpolate the kernel and apply it to the wide- view image so as to get the high-resolution wide-view image. Extensive experiments show that our method achieves favorable performance over state-of-the-art approaches on both quantitative and qualitative evaluations.

Citation Download Citation

Yiman Hou, Qi Li, Huajun Feng, Zhihai Xu, and Yueting Chen "Image super-resolution using CNNs for dual-focal camera system", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 1216641 (15 February 2022); https://doi.org/10.1117/12.2617074

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KEYWORDS

Cameras

Super resolution

Imaging systems

Image restoration

Image resolution

Image processing

Image analysis

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