DPSF-Net: A Dual-Prior Spatial-Frequency Network for Real-World Remote Sensing Image Dehazing
为解决遥感图像去雾问题，提出DPSF-Net网络，利用RGB图像和暗通道先验作为输入，并结合空间-频率特征交互来提高去雾效果。
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Pixel-level Quality Assessment for Oriented Object Detection
Existing rotated object detectors rely on box-level IoU prediction to assess localization quality; however, such metrics exhibit structural coupling with ground-truth localization accuracy, leading to systematic overestimation of low-quality predictions. To address this, we propose Pixel-level Quality Assessment (PQA), a novel framework that abandons box-level IoU regression and instead models the spatial consistency between predicted and ground-truth rotated bounding boxes in pixel space—enabling decoupled and robust localization quality estimation. PQA employs a spatial consistency aggregation mechanism to produce a unified quality score and can be seamlessly integrated as a plug-and-play module into mainstream rotated detectors. Extensive experiments on HRSC2016 and DOTA demonstrate substantial improvements: PQA boosts Rotated RetinaNet’s AP$_{50:95}$ by 5.96% and reduces standard deviation (STD) by 2.32%, validating both the effectiveness and generalizability of pixel-level quality modeling.
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DPSF-Net: A Dual-Prior Spatial-Frequency Network for Real-World Remote Sensing Image Dehazing
为解决遥感图像去雾问题，提出DPSF-Net网络，利用RGB图像和暗通道先验作为输入，并结合空间-频率特征交互来提高去雾效果。
0 citationsRead paper
Pixel-level Quality Assessment for Oriented Object Detection
Existing rotated object detectors rely on box-level IoU prediction to assess localization quality; however, such metrics exhibit structural coupling with ground-truth localization accuracy, leading to systematic overestimation of low-quality predictions. To address this, we propose Pixel-level Quality Assessment (PQA), a novel framework that abandons box-level IoU regression and instead models the spatial consistency between predicted and ground-truth rotated bounding boxes in pixel space—enabling decoupled and robust localization quality estimation. PQA employs a spatial consistency aggregation mechanism to produce a unified quality score and can be seamlessly integrated as a plug-and-play module into mainstream rotated detectors. Extensive experiments on HRSC2016 and DOTA demonstrate substantial improvements: PQA boosts Rotated RetinaNet’s AP$_{50:95}$ by 5.96% and reduces standard deviation (STD) by 2.32%, validating both the effectiveness and generalizability of pixel-level quality modeling.
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