DGD-cGAN: A Dual Generator for Image Dewatering and Restoration
This work addresses the challenge of distorted and low-contrast underwater images for applications like marine research or photography, representing an incremental advancement in image restoration techniques.
The paper tackles the problem of underwater image enhancement by removing haze and color cast to restore true colors, using a dual-generator conditional GAN (DGD-cGAN) that achieves a margin of improvement over state-of-the-art methods on multiple datasets.
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 enhancement approach for dewatering which employs 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 at 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. Our experiments show that DGD-cGAN consistently delivers a margin of improvement as compared with the state-of-the-art methods on several widely available datasets.