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DGD-cGAN: A dual generator for image dewatering and restoration

journal contribution
posted on 2024-01-08, 03:27 authored by S Gonzalez-Sabbagh, Antonio Robles-KellyAntonio Robles-Kelly, Shang GaoShang Gao
Underwater images are usually covered with a blue–greenish colour cast, making them distorted, blurry or low in contrast. This phenomenon occurs due to the light attenuation given by the scattering and absorption in the water column. In this paper we present an image dewatering approach motivated upon the observation that the image formation model can be used to drive the learning process by constraining the loss function and making used of paired data. To this end, we employ a conditional generative adversarial network (cGAN) with two generators. Our Dual Generator Dewatering cGAN (DGD-cGAN) removes the haze and colour cast induced by the water column and restores the true colours of underwater scenes whereby the effects of various attenuation and scattering phenomena that occur in underwater images are tackled by the two generators. The first generator takes as input the underwater image and predicts the dewatered scene, while the second generator learns the underwater image formation process by implementing a custom loss function based upon the transmission and the veiling light components of the image formation model. Extensive experiments show that DGD-cGAN consistently delivers a margin of improvement as compared with state-of-the-art methods on several widely available datasets.

History

Journal

Pattern Recognition

Volume

148

Article number

110159

Pagination

110159-110159

Location

Amsterdam, The Netherlands

ISSN

0031-3203

eISSN

1873-5142

Language

en

Publisher

Elsevier BV

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