Chamber-Reflection-Aware Image Enhancement Method for Powder Spreading Quality Inspection in Selective Laser Melting

In selective laser melting (SLM), real-time visual inspection of powder spreading quality is essential for maintaining dimensional accuracy and mechanical performance. However, reflections from metallic chamber walls introduce non-uniform illumination and reduce local contrast, hindering reliable defect detection. To overcome this problem, a chamber-reflection-aware image enhancement method is proposed, integrating a physical reflection model with a dual-channel deep network. A Gaussian-based curved-surface reflection model is first developed to describe the spatial distribution of reflective interference. The enhancement network then processes the input through two complementary channels: a Retinex-based branch to extract illumination-invariant reflectance components and a principal components analysis (PCA)-based branch to preserve structural information. Furthermore, a noise-aware loss function is designed to suppress the mixed Gaussian–Poisson noise that is inherent in SLM imaging. Experiments conducted on real SLM monitoring data demonstrate that the proposed method significantly improves contrast and defect visibility, outperforming existing enhancement algorithms in peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and natural image quality evaluator (NIQE). The approach provides a physically interpretable and robust preprocessing framework for online SLM quality monitoring.