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Underwater images are often affected by color cast and blurring, which degrade the performance of underwater machine vision tasks. While existing underwater image enhancement (UIE) methods have been proposed to improve image quality for human perception, their effectiveness in enhancing machine vision performance is limited. In this article, a novel unsupervised UIE framework based on disentangled representation (DR) is proposed, which is designed for machine vision tasks. Specifically, the proposed framework disentangles the underwater image into two parts in the latent space according to whether they are beneficial to machine vision tasks: the task-friendly content features and the task-unfriendly distortion features. In addition, a semantic-aware contrastive module (SACM) is employed to alleviate the impact of losing key information required for machine vision tasks using the strategy of contrastive learning. Furthermore, two branches on the features and images are incorporated into the enhancement network, which serve the purpose of delivering task-relevant information to the enhancement model and guide the network to generate task-friendly images. Evaluation of the proposed method is conducted on multiple underwater image datasets, and a comparison is made with state-of-the-art enhancement methods in terms of machine vision performance. The experimental results demonstrate that the proposed method surpasses existing approaches in improving the accuracy and robustness of machine vision tasks, including object detection, semantic segmentation, and saliency detection in underwater environments. Our code is available at https://github.com/gemyumeng/TFUIE.

Clear and high-resolution (HR) underwater images are indispensable in acquiring underwater information. However, existing underwater image enhancement and super-resolution (UIESR) networks achieve limited enhancement-super-resolution performance on real-world turbid low-resolution (LR) underwater images because 1) they assume that the resolution degradation is simple and known bicubic down-sampling, generating unrealistic training data for UIESR task; 2) they extract known priors from the underwater imaging model, which is meager to address complex UIESR problems caused by unknown mixed dual-degradation; and 3) they ignore the interaction between blurring and color casts in the RGB color space, leading to unsatisfactory correction results of two distortions. To address these issues, we propose a realistic UIESR network (RUIESR) consisting of three parts: a realistic LR image generation module (RLGM), a dual-degradation estimation module (DEM), and an enhancement and super-resolution module (ESRM). Firstly, RLGM aims to generate LR images obeying underwater LR image distribution by learning real LR properties from unpaired real LR-HR underwater images for training. Secondly, a contrast-driven learning strategy is proposed in the DEM to accurately estimate unknown dual-degradation priors that can aid the reconstruction task. Finally, ESRM is proposed to enhance textures and correct color casts, which includes a dual-branch structure to separate blurring and color casts distortions and utilizes specific priors for each distortion to assist reconstruction. Extensive experiments on real and synthetic underwater datasets show that the proposed RUIESR outperforms existing works regarding visual quality and quantitative metrics.

Machine analysis of underwater images is essential to most underwater applications. However, both the limitation of communication bandwidth and underwater degradation bring much difficulty to accurate machine recognition at the end system. Few existing underwater compression methods consider unique underwater prior knowledge to better serve for machine vision under low bit-rates. To address this problem, we propose a novel underwater image compression framework for machines, which utilizes underwater priors to contrastively enhance degraded features by contrastive learning and efficiently compress machine-friendly features under low bit-rates. A dataset is built to provide positive and negative samples for contrastive learning based on machine analysis performance. At the encoder side, a feature extractor and a feature encoder are employed to extract machine-related features and compress them into compact representations. To alleviate the effect of underwater degradation on machine vision, a prior-guided contrastive feature enhancement module is proposed to learn more machine-friendly features based on positive and negative samples from our dataset. Then a feature refinement block is designed to remove channel-wise redundancy and focus spatial-wise importance based on high similarity of machine-related features and characteristics of underwater images. More compact representations are obtained without degrading analysis performance under low bit-rates. At the decoder side, both machine-friendly features and image are reconstructed to support different types of analysis tasks. Experimental results demonstrate the superiority of our framework in machine vision tasks compared with traditional compression methods and learned-based methods. Besides, our method still preserves basic capability of human perception.