Zero-Reference Depth Curve Estimation-Based Low-Light Image Enhancement Method for Coating Workshop Inspection

doi:10.3390/coatings15040478

Zero-Reference Depth Curve Estimation-Based Low-Light Image Enhancement Method for Coating Workshop Inspection

2025-04-17

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DOI：10.3390/coatings15040478

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To address the challenges of poor image quality and low detection accuracy in low-light environments during coating workshop inspections, this paper proposes a low-light image enhancement method based on zero-reference depth curve estimation, termed Zero-PTDCE. A low-light image dataset, PT-LLIE, tailored for coating workshop scenarios is constructed, encompassing various industrial inspection conditions under different lighting environments to enhance model adaptability. Furthermore, an enhancement network integrating a lightweight denoising module and depthwise separable dilated convolution is designed to reduce noise interference, expand the receptive field, and improve image detail restoration. The network training process employs a multi-constraint strategy by incorporating perceptual loss (Lp), color loss (Lc), spatial consistency loss (Ls), exposure loss (Le), and total variation smoothness loss (Ltv) to ensure balanced brightness, natural color reproduction, and structural integrity in the enhanced images. Experimental results demonstrate that, compared to existing low-light image enhancement methods, the proposed approach achieves superior performance in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE), while maintaining high computational efficiency. Beyond general visual enhancement, Zero-PTDCE significantly improves the visibility of fine surface features and defect patterns under low-light conditions, which is crucial for the accurate assessment of coating quality, including defect identification such as uneven thickness, delamination, and surface abrasion. This work provides a reliable image enhancement solution for intelligent inspection systems and supports both the automated operation and material quality evaluation in modern coating workshops, contributing to the broader goals of intelligent manufacturing and material characterization.

Keywords:

coating workshop inspection

zero-reference learning

deep curve estimation

low-light image enhancement

Zero-Reference Depth Curve Estimation-Based Low-Light Image Enhancement Method for Coating Workshop Inspection

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