Super-resolution reconstruction of GAN pest and disease images fused with attention mechanisms

doi:10.14088/j.cnki.issn0439-8114.2024.09.034

Abstract

Abstract: The sample pictures of coffee and citrus pests and diseases were collected, and an attention module was added to the super-resolution reconstruction network of the original SRGAN by using TensorFlow deep learning framework. The visual quality, peak signal-to-noise ratio and structured similarity index of the reconstructed image were analyzed. The results showed that the peak signal-to-noise ratio of the designed model was improved by 2.23, and the structural similarity was enhanced by 7%, after comparing with the original SRGAN mode. Better visuals could be obtained in terms of detail texture, and the accuracy of the reconstructed image classification was improved by about 4.42 percentage points. Therefore, the model designed could be used for the expansion of samples of plant pests and diseases with small sample properties.

Key words: super-resolution reconstruction, attention mechanism, pests and diseases, peak signal-to-noise ratio （PSNR）, structural similarity（SSIM）

CLC Number:

FEI Jia-jie, YANG Yi, ZENG Yan-lin, LIN Yao, HE Yi-ting, LI Qiang, ZHANG Sheng-di. Super-resolution reconstruction of GAN pest and disease images fused with attention mechanisms[J]. HUBEI AGRICULTURAL SCIENCES, 2024, 63(9): 204-209.

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