Printed circuit board (PCB) defect detection is critical for ensuring the safety of electronic devices, especially in the space industry. Non-reference-based methods, typically the deep learning methods, suffer from a large amount of annotated data requirements and poor interpretability. In contrast, conventional reference-based methods achieve higher detection accuracy by comparing with a template image but rely on precise image alignment and face the challenge of fine defects detection. To solve the problem, we propose a novel Edge-guided Energy-based PCB Defect Detection method (EEDD). We focus on the salient edge characteristic of PCB images and regard the functional defects as contour differences and then propose a novel energy measurement method for PCB contour anomaly detection. We introduce the energy transformation using the edge information of the template and test image, then Speeded-Up Robust Features method (SURF) is used for image alignment, and finally achieve defect detection by measuring the energy anomaly score pixel by pixel with the proposed energy-based defect localization and contour flood fill methods. Our method excels in detecting multi-scale defects, particularly tiny defects, and is robust against interferences such as non-finely aligned images and edge spurs. Experiments on the DeepPCB-A dataset and our high-resolution PCB dataset (HDPCB) show that the proposed method outperforms state-of-the-art methods in PCB defect-detection tasks.

Infrared and visible image fusion (IVIF) aims to render fused images that maintain the merits of both modalities. To tackle the challenge in fusing cross-modality information and avoiding texture loss in IVIF, we propose a novel edge-consistent and correlation-driven fusion framework (ECFuse). This framework leverages our proposed edge-consistency fusion module to maintain rich and coherent edges and textures, simultaneously introducing a correlation-driven deep learning network to fuse the cross-modality global features and modality-specific local features. Firstly, the framework employs a multi-scale transformation (MST) to decompose the source images into base and detail layers. Then, the edge-consistent fusion module fuses detail layers while maintaining the coherence of edges through consistency verification. A correlation-driven fusion network is proposed to fuse the base layers containing both modalities’ main features in the transformation domain. Finally, the final fused spatial image is reconstructed by inverse MST. We conducted experiments to compare our ECFuse with both conventional and deep leaning approaches on TNO, LLVIP and ${\mathrm{M}}^3\mathrm{FD}$ datasets. The qualitative and quantitative evaluation results demonstrate the effectiveness of our framework. We also show that ECFuse can boost the performance in downstream infrared–visible object detection in a unified benchmark.

Infrared and visible image fusion (IVIF) aims to integrate salient targets and detailed information into a single image suitable for both human perception and machine processing. However, many existing methods rely on human-designed fusion rules, which lack interpretability and often fail to effectively enhance detail parts. To address these issues, we propose an IVIF method based on correlation-driven fusion rules and a parameter-free attention module. This method retains valid information across layers and modalities, with fusion weights derived from cross-modal feature map correlations, enhancing robustness. We also introduce a parameter-free attention module to adaptively enhance texture details without additional trainable parameters. Experimental results on public datasets demonstrate the superiority of our method in detail retention and target highlighting, with quantitative evaluations showing improvements in metrics such as average gradient (AG) and Visual Information Fidelity (VIF) by up to 12% and 18% respectively, compared to state-of-the-art methods on the LLVIP dataset. The source code will be released at https://github.com/CharlesShan-hub/CPFusion.