Multi-Scale Supervised Dual-Layer Generative Adversarial Network: A Method for Region Restoration of LCM Images Degraded by Exposure Issues
Longhu Huang, Sheng Zheng*
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, China
* Corresponding Author: Sheng Zheng. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.082622
Received 19 March 2026; Accepted 22 May 2026; Published online 15 June 2026
Abstract
In the field of online automated defect inspection for small-size liquid crystal display modules (LCMs), the accuracy of module loading is crucial for the subsequent lighting inspection. However, due to the physical characteristics of the module’s flexible ribbon cable, the ribbon often exhibits varying degrees of curling, causing conventional monocular vision systems to frequently encounter local underexposure or overexposure when positioning the workpiece, resulting in loss of local details and significantly affecting subsequent positioning and loading. To address the problem of local image degradation caused by abnormal exposure, this study proposes a regional image generation method based on a dual-layer generative adversarial network (GAN) with multi-scale supervision. This method first uses a mask localization module to restrict the region for image generation, then employs multi-scale local generation and adversarial learning to produce high-fidelity images in areas with local exposure anomalies, and finally uses a newly added global discriminator to regulate the edges of the generated images, allowing the generated images to smoothly connect with the original images, thereby achieving local image repair. Compared to single-layer GAN models, the repaired overall image achieved a peak signal-to-noise ratio (PSNR) improvement of 3.01% and a structural similarity (SSIM) improvement of 11.7%, while the PSNR of the repaired images in exposure-anomalous regions increased by 49.04% and SSIM increased by 23.18%. In addition, not only does the model produce restored images with excellent visual effects, but through verification by deployment on an actual factory production line, the error between the restored images and normal samples is within 0.08 mm, validating the model’s value in practical industrial applications.
Keywords
Image restoration; multi-scale supervision; GAN; LCM
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