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- W4308234206 abstract "With the increasing availability of portable cameras and smart phones, directly estimating PM <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2.5</inf> based on digital photography shows advantages in efficiency and economic costs. In this paper, a novel Two-stream Non-uniform Concentration Reasoning Network (TNCR-Net) is proposed for single image PM <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2.5</inf> concentration estimation. Motivated by locally non-uniform particle pollution concentration distribution in images, we adopt patch-based scheme and adaptive weighted average mechanism to obtain patch-wise concentration and relative weight based on spatially varying perceptual relevance of local particle pollution concentration. Then aggregate patch-wise concentrations according to relative weights. To learn more effective feature from particular pollution image, we use a two-stream network structure with the dark channel map as the input of one stream. Besides, we employ attention-based feature fusion method to flexibly aggregate the feature maps of the two streams. Experiments on real-world dataset indicate that our TNCR-Net outperforms other state-of-the-art methods with fewer parameters." @default.
- W4308234206 created "2022-11-09" @default.
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- W4308234206 date "2022-10-16" @default.
- W4308234206 modified "2023-09-30" @default.
- W4308234206 title "Two-Stream Non-Uniform Concentration Reasoning Network for Single Image Air Pollution Estimation" @default.
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- W4308234206 doi "https://doi.org/10.1109/icip46576.2022.9897665" @default.
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