All-In-One Underwater Image Enhancement using Domain-Adversarial Learning
This addresses the challenge of enhancing diverse underwater images for vision systems, but it is incremental as it builds on existing domain adaptation techniques.
The paper tackles the problem of enhancing underwater images degraded by varying water types by proposing a domain-adversarial learning model that disentangles water-type nuisances to generate enhanced images, resulting in improved SSIM and PSNR scores across multiple water types and better generalization to real-world datasets.
Raw underwater images are degraded due to wavelength dependent light attenuation and scattering, limiting their applicability in vision systems. Another factor that makes enhancing underwater images particularly challenging is the diversity of the water types in which they are captured. For example, images captured in deep oceanic waters have a different distribution from those captured in shallow coastal waters. Such diversity makes it hard to train a single model to enhance underwater images. In this work, we propose a novel model which nicely handles the diversity of water during the enhancement, by adversarially learning the content features of the images by disentangling the unwanted nuisances corresponding to water types (viewed as different domains). We use the learned domain agnostic features to generate enhanced underwater images. We train our model on a dataset consisting images of 10 Jerlov water types. Experimental results show that the proposed model not only outperforms the previous methods in SSIM and PSNR scores for almost all Jerlov water types but also generalizes well on real-world datasets. The performance of a high-level vision task (object detection) also shows improvement using enhanced images with our model.