Infrared image super-resolution reconstruction based on closed-loop regression network

Minqi Wang; Bowen Wang; Linfei Zhang; Yuhai Li

doi:10.1117/12.2602503

9 October 2021 Infrared image super-resolution reconstruction based on closed-loop regression network

Minqi Wang, Bowen Wang, Linfei Zhang, Yuhai Li

Proceedings Volume 11896, Advanced Optical Imaging Technologies IV; 1189617 (2021) https://doi.org/10.1117/12.2602503
Event: SPIE/COS Photonics Asia, 2021, Nantong, JS, China

Conference Poster

Abstract

The wide application of the image super-resolution algorithms significantly improves the visual quality of infrared images. In this paper, an infrared image super-resolution reconstruction method based on a closed-loop regression network is proposed. The residual channel attention block is introduced into the up-sampling module group, which effectively improves the capacity of the network and increases the number of feature maps, enhances the extraction and recovery ability of infrared image features, and is conducive to the recovery of image details. Compared with other infrared information recovery methods previously proposed, the proposed method has obvious advantages in high-resolution detail acquisition capability. Neural network through closed-loop regression, this scheme overcomes the LR image to HR image defects in nonlinear mapping function, by introducing additional constraints on the LR data to reduce the space of the possible functions.

Conference Presentation

Citation Download Citation

Minqi Wang, Bowen Wang, Linfei Zhang, and Yuhai Li "Infrared image super-resolution reconstruction based on closed-loop regression network", Proc. SPIE 11896, Advanced Optical Imaging Technologies IV, 1189617 (9 October 2021); https://doi.org/10.1117/12.2602503

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