JWCDN embeds a simplified underwater image formation model into a GAN with multi-scale dense encoder-decoder and edge-preserving modules to jointly compensate wavelength attenuation and dehaze images, trained via a novel synthesis method simulating different water types.
Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We collected a dataset of images taken in different locations with varying water properties, showing color charts in the scenes. Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging. This dataset enables a quantitative evaluation of restoration algorithms on natural images and shows the advantage of our method.
years
2019 2representative citing papers
A comprehensive survey of deep learning-based underwater image enhancement with systematic experimental comparison of algorithms on multiple datasets.
citing papers explorer
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Jointly Adversarial Network to Wavelength Compensation and Dehazing of Underwater Images
JWCDN embeds a simplified underwater image formation model into a GAN with multi-scale dense encoder-decoder and edge-preserving modules to jointly compensate wavelength attenuation and dehaze images, trained via a novel synthesis method simulating different water types.
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Diving Deeper into Underwater Image Enhancement: A Survey
A comprehensive survey of deep learning-based underwater image enhancement with systematic experimental comparison of algorithms on multiple datasets.