Underwater Image Color Correction Using Ensemble Colorization Network

Arpit Pipara, Urvi Oza, Srimanta Mandal; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 2011-2020

Underwater image color correction has been gaining traction due to its usage in marine biology and surveillance. Color corrected images also help marine archaeologists in locating objects. The underwater image suffers from various degradation with respect to the depth at which the image is taken. In this paper, we propose an alternate path to correct the color of the underwater images. We address the problem of underwater image color correction as a colorization task. For this purpose, we propose a deep learning architecture that comprises of an ensemble encoder and a decoder. The ensemble encoder part uses pre-trained networks to extract multi-level features. These features are then fused together and are used up by the decoder to generate the color corrected output. We evaluate the performance of our model using reference-based as well as no reference-based metrics. The metrics indicate that the produced results are inline with the human perceptual system.