Zero-UMSIE: a zero-shot underwater multi-scale image enhancement method based on isomorphic features.

Due to the scattering and absorption of light, underwater images often exhibit degradation. Given the scarcity of paired real-world data and the inability of synthetic paired data to perfectly approximate real-world data, it's a challenge to restore these degraded images using deep neural networks. In this paper, a zero-shot underwater multi-scale image enhancement method (Zero-UMSIE) is proposed, which utilizes the isomorphism between the original underwater image and the re-degraded image. Specifically, Zero-UMSIE first estimates three latent components of the original underwater image: the global background light, the transmission map, and the scene radiance. Then, the estimated scene radiance is randomly mixed with the original underwater image to generate re-degraded images. Finally, a multi-scale loss and a set of tailored non-reference loss functions are employed to fine-tune the underwater image and enhance the generalization ability of the network. These functions implicitly control the learning preferences of the network and effectively address issues such as color bias and uneven illumination in underwater images, without the need for additional datasets. The proposed method is evaluated on three widely used real-world underwater image datasets. Extensive experiments on various benchmarks demonstrate that the proposed method is superior to state-of-the-art methods subjectively and objectively, which is competitive and applicable to diverse underwater conditions.