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By leveraging self-attention mechanisms and residual convolution within the SRGAN framework, the model enhances image resolution, reduces noise, and uncovers finer details, achieving superior PSNR (37.17) and SSIM (0.9590) metrics. Integrated with an improved YOLOv8-ghost-p2 framework, the model also attains a mean Average Precision (mAP) of 81.2 and a recall rate of 76 in defect detection. 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