Underwater Image Enhancement with Cascaded Contrastive Learning

• URL: http://arxiv.org/abs/2411.10682v1
• Date: Sat, 16 Nov 2024 03:16:44 GMT
• Title: Underwater Image Enhancement with Cascaded Contrastive Learning
• Authors: Yi Liu, Qiuping Jiang, Xinyi Wang, Ting Luo, Jingchun Zhou,
• Abstract summary: Underwater image enhancement (UIE) is a highly challenging task due to the complexity of underwater environment and the diversity of underwater image degradation. Most of the existing deep learning-based UIE methods follow a single-stage network which cannot effectively address the diverse degradations simultaneously. We propose a two-stage deep learning framework and taking advantage of cascaded contrastive learning to guide the network training of each stage.
• Score: 51.897854142606725
• License:
• Abstract: Underwater image enhancement (UIE) is a highly challenging task due to the complexity of underwater environment and the diversity of underwater image degradation. Due to the application of deep learning, current UIE methods have made significant progress. Most of the existing deep learning-based UIE methods follow a single-stage network which cannot effectively address the diverse degradations simultaneously. In this paper, we propose to address this issue by designing a two-stage deep learning framework and taking advantage of cascaded contrastive learning to guide the network training of each stage. The proposed method is called CCL-Net in short. Specifically, the proposed CCL-Net involves two cascaded stages, i.e., a color correction stage tailored to the color deviation issue and a haze removal stage tailored to improve the visibility and contrast of underwater images. To guarantee the underwater image can be progressively enhanced, we also apply contrastive loss as an additional constraint to guide the training of each stage. In the first stage, the raw underwater images are used as negative samples for building the first contrastive loss, ensuring the enhanced results of the first color correction stage are better than the original inputs. While in the second stage, the enhanced results rather than the raw underwater images of the first color correction stage are used as the negative samples for building the second contrastive loss, thus ensuring the final enhanced results of the second haze removal stage are better than the intermediate color corrected results. Extensive experiments on multiple benchmark datasets demonstrate that our CCL-Net can achieve superior performance compared to many state-of-the-art methods. The source code of CCL-Net will be released at https://github.com/lewis081/CCL-Net.

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