Papers › Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing

Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing

12 May 2020IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2020 6arXiv:2005.05999archive 2025-07-28

Sourya Dipta Das, Saikat Dutta

Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing. Apart from that, existing popular Multi-scale approaches are runtime intensive and memory inefficient. In this context, we proposed a fast Deep Multi-patch Hierarchical Network to restore Non-homogeneous hazed images by aggregating features from multiple image patches from different spatial sections of the hazed image with fewer number of network parameters. Our proposed method is quite robust for different environments with various density of the haze or fog in the scene and very lightweight as the total size of the model is around 21.7 MB. It also provides faster runtime compared to current multi-scale methods with an average runtime of 0.0145s to process 1200x1600 HD quality image. Finally, we show the superiority of this network on Dense Haze Removal to other state-of-the-art models.

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diptamath/Nonhomogeneous_Image_Dehazing mentioned on GitHubpytorchMIT report
saikatdutta/Stacked_DMSHN_bokeh mentioned on GitHubpytorchMIT report

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spatial_average saikatdutta/Stacked_DMSHN_bokeh/PerceptualSimilarity/models/networks_basic.py community (archive-listed) ran · our draft was wrong fingerprinted MIT (permissive) · 29e5e72bcd006dcd · report
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load_image saikatdutta/Stacked_DMSHN_bokeh/PerceptualSimilarity/util/util.py community (archive-listed) unverified MIT (permissive) · 722a1d429018eccd · report
score_2afc_dataset saikatdutta/Stacked_DMSHN_bokeh/PerceptualSimilarity/models/dist_model.py community (archive-listed) unverified MIT (permissive) · 514dd3c0eee3691c · report
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Image DehazingNonhomogeneous Image Dehazing

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