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A Robust Facial Image Restoration Support System for Human Environment Security Based on Generative Adversarial Networks with Hybrid and Multi-scale Spatial Attention Mechanisms

Year: 2026
Type: article
Abstract: In intelligent human environments, image sensors are frequently hindered by occlusions, such as masks, which degrade the reliability of facial data for security and interactive support systems.In this paper, we propose a unified framework designed as a robust support system that integrates an occlusion segmentation network with a face image restoration network.To facilitate deployment in resource-constrained sensing nodes, the segmentation network employs depthwise separable convolutions to ensure computational efficiency while leveraging residual connections for multi-scale feature fusion.On the basis of precisely localized occluded areas, a generative adversarial network is introduced to reconstruct facial structures with high fidelity.The generator incorporates two novel feature enhancement components: a hybrid attention aggregation module, which strengthens global semantic consistency within skip connections, and a multi-scale spatial attention module, designed to capture fine-grained textures from sensor data across different spatial scales.Experimental results on the CelebFaces Attributes High-Quality (CelebA-HQ) dataset demonstrate that the proposed system effectively restores masked facial regions, achieving a PSNR of 35.01 dB and an SSIM of 0.931 under challenging 35-45% occlusion ratios.By significantly enhancing visual fidelity and recognition robustness, this framework provides a reliable solution for real-world vision-based support systems in humancentric environments.
DOI: 10.18494/sam6179
Language: English
Open Access status: gold
Estimated APC paid: $748
OpenAlex ID: W7164990531
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