Joint Luminance and Chrominance Learning for Underwater Image Enhancement

Recently, learning-based works have been widely-investigated to enhance underwater images. However, interactions between various degradation factors (e.g., color distortion and haze effects) inevitably cause negative interference during the inference phase. Thus, these works cannot fully remove degraded factors. To address this problem, we propose a novel Joint Luminance and Chrominance Learning Network (JLCL-Net). Concretely, we reformulate the task as luminance reconstruction (for haze removal), and chrominance correction (for color correction) sub-tasks by separating the luminance and chrominance (i.e., color appearance) of the underwater images. In this way, we successfully realize the disentanglement in degraded factors to avoid introducing interference. We specify the reconstruction by integrating the atmospheric scattering model, which endows the adaptive dehazing ability over different scenarios. The correction learns to compensate for color by a simple network to reverse the color attenuation process. To this end, we obtain our JLCL-Net. To better train it, we design a new multi-stage cross-space training strategy, which progressively updates the network parameters to enlarge the network potentiality. Extensive evaluations are presented to fully verify our superiority against other methods.