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Based on improved cycle generative adversarial network for infrared image generation
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1.School of Information Engineering,Shenyang University of Chemical Technology，Shenyang 110142, China; 2.Shenyang Institute of Automation, Chinese Academy of Sciences，Shenyang 110016, China; 3.Key Laboratory of OpticalElectronics Information Technology Processing, Shenyang Institute of Automation, Chinese Academy of Sciences，Shenyang 110016， China; 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences，Shenyang 110169, China

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TP391

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    Abstract:

    To address the problem that the existing algorithms for generating infrared images from visible images cannot perceive the weak texture regions of the images, which leads to the low quality of the generated image details, this paper proposes an improved Cycle Generation Adversarial Network (CycleGAN) for the image generation task. structure for image generation tasks. Firstly, the generator network structure of the cycle generation adversarial network is constructed by using the residual network with stronger feature extraction ability, so that the image features can be fully extracted and the problem of low image quality caused by insufficient feature extraction can be solved; secondly, the channel attention mechanism and spatial attention mechanism are introduced in the generator network structure, and the regions with poor image perception are weighted by the attention mechanism to solve the problem of loss of image texture details. processing to solve the problem of image texture detail loss. On the OSU dataset, the proposed method improves the Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics by 7.1% and 10.9%, respectively, compared with the cyclic generative adversarial network method on the Flir dataset. PSNR and SSIM improved by 4.0% and 6.7%, respectively, on the Flir dataset. The experimental results on several datasets demonstrate that the improved method in this paper can highlight the detailed feature information in the image generation task and can effectively improve the quality of image generation.

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  • Online: January 10,2024
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