Enhancing Underwater Imaging with 4-D Light Fields: Dataset and Method

• URL: http://arxiv.org/abs/2408.17339v1
• Date: Fri, 30 Aug 2024 15:06:45 GMT
• Title: Enhancing Underwater Imaging with 4-D Light Fields: Dataset and Method
• Authors: Yuji Lin, Xianqiang Lyu, Junhui Hou, Qian Zhao, Deyu Meng,
• Abstract summary: 4-D light fields (LFs) enhance underwater imaging plagued by light absorption, scattering, and other challenges. We propose a progressive framework for underwater 4-D LF image enhancement and depth estimation. We construct the first 4-D LF-based underwater image dataset for quantitative evaluation and supervised training of learning-based methods.
• Score: 77.80712860663886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we delve into the realm of 4-D light fields (LFs) to enhance underwater imaging plagued by light absorption, scattering, and other challenges. Contrasting with conventional 2-D RGB imaging, 4-D LF imaging excels in capturing scenes from multiple perspectives, thereby indirectly embedding geometric information. This intrinsic property is anticipated to effectively address the challenges associated with underwater imaging. By leveraging both explicit and implicit depth cues present in 4-D LF images, we propose a progressive, mutually reinforcing framework for underwater 4-D LF image enhancement and depth estimation. Specifically, our framework explicitly utilizes estimated depth information alongside implicit depth-related dynamic convolutional kernels to modulate output features. The entire framework decomposes this complex task, iteratively optimizing the enhanced image and depth information to progressively achieve optimal enhancement results. More importantly, we construct the first 4-D LF-based underwater image dataset for quantitative evaluation and supervised training of learning-based methods, comprising 75 underwater scenes and 3675 high-resolution 2K pairs. To craft vibrant and varied underwater scenes, we build underwater environments with various objects and adopt several types of degradation. Through extensive experimentation, we showcase the potential and superiority of 4-D LF-based underwater imaging vis-a-vis traditional 2-D RGB-based approaches. Moreover, our method effectively corrects color bias and achieves state-of-the-art performance. The dataset and code will be publicly available at https://github.com/linlos1234/LFUIE.

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