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• W2955731188 abstract "Single image dehazing is a critical image pre-processing step for subsequent high-level computer vision tasks. However, it remains challenging due to its ill-posed nature. Existing dehazing models tend to suffer from model overcomplexity and computational inefficiency or have limited representation capacity. To tackle these challenges, here we propose a fast and accurate multi-scale end-to-end dehazing network called FAMED-Net, which comprises encoders at three scales and a fusion module to efficiently and directly learn the haze-free image. Each encoder consists of cascaded and densely connected point-wise convolutional layers and pooling layers. Since no larger convolutional kernels are used and features are reused layer-by-layer, FAMED-Net is lightweight and computationally efficient. Thorough empirical studies on public synthetic datasets (including RESIDE) and real-world hazy images demonstrate the superiority of FAMED-Net over other representative state-of-the-art models with respect to model complexity, computational efficiency, restoration accuracy, and cross-set generalization. The code will be made publicly available." @default.
• W2955731188 created "2019-07-12" @default.
• W2955731188 creator A5001819736 @default.
• W2955731188 creator A5028981937 @default.
• W2955731188 date "2020-01-01" @default.
• W2955731188 modified "2023-10-17" @default.
• W2955731188 title "FAMED-Net: A Fast and Accurate Multi-Scale End-to-End Dehazing Network" @default.
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• W2955731188 doi "https://doi.org/10.1109/tip.2019.2922837" @default.
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• W2955731188 hasPublicationYear "2020" @default.
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