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Adaptive Patch Based Convolutional Neural Network for Robust Dehazing | IEEE Conference Publication | IEEE Xplore

Adaptive Patch Based Convolutional Neural Network for Robust Dehazing


Abstract:

We present a novel deep learning-based dehazing method using adaptive patch splits. Our method applies quad-tree decomposition to an input image, yielding multiple patche...Show More

Abstract:

We present a novel deep learning-based dehazing method using adaptive patch splits. Our method applies quad-tree decomposition to an input image, yielding multiple patches with adaptive sizes. Then, each patch is fed into a Convolutional Neural Network (CNN) and classified into a single transmission value, in which a transmission map comprises transmission values from all patches. Homogeneous regions in the image are typically decomposed into large patches. Thus the method can save computational cost. Non-homogeneous regions are divided into small patches, which helps preserve local details in a transmission map. To train CNN, we synthesize numerous hazy images from haze-free images. Experimental results demonstrate our method surpasses state- of-the-art deep learning based algorithms quantitatively and qualitatively.
Date of Conference: 07-10 October 2018
Date Added to IEEE Xplore: 06 September 2018
ISBN Information:
Electronic ISSN: 2381-8549
Conference Location: Athens, Greece

References

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