single image dehazing
With the fast growth of deep learning, trainable frameworks have been presented to restore hazy images. However, the capability of most existing learning-based methods is limited since the parameters learned in an end-
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This folder contains folders of images.
The original folder contains the non dehazed images, default and tuned contain the dehazed counterparts.
Default folder referes to the outputs obtained using an exponent of 0.8.
Tuned refers to the images with a PSNRBR of 54 or above.
The images in paper are kept in a seperate folder.
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<p>Images from Sentinel 2 for dehazing. Contains 3 folders, one with original images, one with dehazed images at default exponent of 0.8 and the last with failed images with fine tuned exponent (thus becoming successful).</p>
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We proposed a new dataset, HazeRD, for benchmarking dehazing algorithms under realistic haze conditions. As opposed to prior datasets that made use of synthetically generated images or indoor images with unrealistic parameters for haze simulation, our outdoor dataset allows for more realistic simulation of haze with parameters that are physically realistic and justified by scattering theory.
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