Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset

TLDR

Underwater images suffer from color distortion and low contrast because light attenuation varies with wavelength, water type, and scene geometry. The authors propose a single‑image restoration method that incorporates multiple spectral profiles of different water types and release a dataset of 57 images. By estimating two global attenuation ratios (blue‑red and blue‑green) the problem is reduced to single‑image dehazing, and the method evaluates parameters from a library of water types to automatically select the best restoration based on color distribution. The dataset enables the first rigorous quantitative evaluation of underwater image restoration algorithms on natural images.

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 also contribute a dataset of 57 images taken in different locations. To obtain ground truth, we placed multiple color charts in the scenes and calculated its 3D structure using stereo imaging. This dataset enables a rigorous quantitative evaluation of restoration algorithms on natural images for the first time.