| Citation: | ZHAO Tian-yuan, DONG Deng-feng, ZHOU Wei-hu, WANG Guo-ming. Micro LED Visible Light and Photoluminescence Image Sub-pixel Fast Registration[J]. Chinese Optics. doi: 10.37188/CO.2025-0142
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To address the challenge of achieving high-precision registration between visible light (RGB) and photoluminescence (PL) images in Micro LED defect inspection, which arises from substantial modality differences, this study introduces a robust multimodal image registration approach capable of attaining sub-pixel accuracy, aiming to establish a direct mapping between the physical structure and electrical characteristics of the chips. We propose a registration method that integrates structural feature constraints with bidirectional residual optimization. First, leveraging the geometric regularity of Micro LED arrays, a tailored feature detection strategy is employed: electrode centers in RGB images are accurately extracted via ellipse fitting and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), while chip centers in PL images are localized using an enhanced watershed algorithm with sub-pixel refinement. Second, during the registration optimization stage, a bidirectional residual constraint framework is constructed, incorporating a confidence weighting mechanism derived from residual distribution analysis. The optimal affine transformation parameters are then estimated using an iterative reweighted least squares method. Experimental results demonstrate that the proposed method achieves sub-pixel-level accuracy, with a mean absolute error (MAE) of 0.823 pixels, representing a 94.2% reduction compared to baseline methods. The root mean square error (RMSE) is 0.996 pixels, the maximum error remains below 2.839 pixels, and the inlier rate attains 75.0%. Each registration process takes only 0.036 seconds on average, achieving an order-of-magnitude improvement in computational efficiency over traditional mutual information (MI) methods. By effectively mitigating feature mismatch and outlier interference in multimodal images, the proposed method outperforms conventional approaches in terms of registration accuracy, robustness, and efficiency, thereby providing a reliable technical foundation for precise defect detection and multimodal analysis of Micro LED chips.
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