Underwater image enhancement and super-resolution using implicit neural networks

Underwater images are often notably degraded by light scattering and absorption. To improve image quality and object details, we present a novel unsupervised underwater image enhancement and super-resolution method using implicit neural networks. Concretely, taking low-resolution coordinates as the inputs, we first leverage Fourier feature mapping to encode the coordinates. Then, three implicit neural networks are applied to estimate each component (i.e., the global background light, the transmission map, and the scene radiance) of the underwater formation model. Those components are further used to reconstruct the raw underwater image in a self-supervised fashion. In the inference stage, high-resolution coordinates are employed to predict a high-quality and high-resolution underwater image. Extensive experiments show that our method achieves a favorable performance in terms of both super-resolution and quality enhancement as compared with current approaches.