Polarization image fusion algorithm based on gradient residual generation adversarial network

Guangqiu Chen; Wenqing Yin; Qizhang Wen; Chenjie Zhang

doi:10.1117/12.3021358

19 February 2024 Polarization image fusion algorithm based on gradient residual generation adversarial network

Guangqiu Chen, Wenqing Yin, Qizhang Wen, Chenjie Zhang

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130632U (2024) https://doi.org/10.1117/12.3021358
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

Aiming at the problem that traditional visible light imaging systems are easily affected by environmental factors and cannot accurately describe the scene information, a gradient residual generative adversarial network for polarization image fusion is proposed in this paper. In the generator network structure, dense blocks are added to the encoder part to retain more features, the gradient residual module is constructed for fusing the feature maps to enhance the texture and detail features of the image, and the multi-scale weighted structural similarity loss function and gradient loss function are designed to improve the network performance. The experimental results show that the method in this paper obtains fused images with richer texture structure and more in line with the visual sense perception of the human eye, and at the same time has the optimal objective evaluation index.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guangqiu Chen, Wenqing Yin, Qizhang Wen, and Chenjie Zhang "Polarization image fusion algorithm based on gradient residual generation adversarial network", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130632U (19 February 2024); https://doi.org/10.1117/12.3021358

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