Change detection performance in remote sensing is highly sensitive to illumination variations between bi-temporal images. In real-world scenarios, low-light conditions and inconsistent brightness often lead to degraded feature representations and unreliable detection results. To address this issue, this paper proposes a Consistent Photometric Enhancement Network (CPEN) for change detection under complex illumination conditions. Unlike conventional low-light enhancement methods applied independently to each image, CPEN explicitly enforces photometric consistency between bi-temporal images before and during an enhancement procedure. The proposed framework applies a low-light compensation mechanism to reduce photometric discrepancies between image pairs, which is followed by a bi-temporal enhancement module that jointly improves brightness while preserving shared structural information. Using the photometrically consistent and enhanced images, a change detection module is employed to extract reliable features and generate accurate change maps. Experimental results show that CPEN achieves F1/IoU scores of 90.39/82.67, 80.71/70.58, 78.34/64.26, and 86.26/79.02 on LEVIR-CD, PRCV-CD, SYSU-CD, and the real-world NPULL-CD dataset, respectively, demonstrating its robustness under both simulated and real illumination variations.
Pengcheng Han, Zhenyu Xia, Lin Chen et al.· Remote Sensing· 0 citations
Computed Tomography (CT) scans are widely used to diagnose lung infections; however, manual interpretation is labor-intensive. Artificial intelligence has accelerated the development of computer-aided diagnostic (CAD) systems, allowing faster and more accurate diagnosis. Nevertheless, many existing CAD systems lack robust cross-dataset generalization and interpretability, limiting their reliability and resulting in suboptimal diagnostic performance. To address these limitations, we propose a semantic attention-driven retrieval framework based on a lightweight Meta-Domain Adaptive Segmentation Network (MDA-SN) with an adaptive data normalization strategy to enhance infection detection in cross-dataset analysis. This framework quantifies infection ratios and retrieves relevant CT slices from the database, closely matching the input test sample to further support medical experts in making more accurate diagnostic decisions. The MDA-SN design leverages multi-scale dilated grouped convolution with residual attention to ensure real-time performance while maintaining accuracy. Our framework achieved an average cross-dataset performance of 75.93% Dice index and 67.42% Intersection over Union, surpassing state-of-the-art methods by 3.32% and 3.28%, respectively. Additionally, it achieves real-time execution, processing an average of 29 slices per second, due to its significantly reduced number of training parameters, approximately 70% fewer than its closest competitor. The implementation and materials are available at our GitHub repository:
https://github.com/Owais-CodeHub/MDA-SN
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Muhammad Owais, Taimur Hassan, Naqash Afzal et al.· Scientific Reports· 1 citation
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