一种基于补丁的方法,用于用Denoising扩散模型进行水下图像增强
IEEE transactions on cybernetics
|October 30, 2024
概括
这项研究引入了一种新方法,使用消噪扩散模型来增强模糊和色彩扭曲的水下图像,提高海洋研究的清晰度.
科学领域:
- 海洋生物学 海洋生物学
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 水下图像由于光散射和吸收而遭受模糊和色彩扭曲.
- 这些退化阻碍了海洋研究和勘探任务.
- 现有的图像增强技术往往无法应对这些具体挑战.
研究的目的:
- 提出一种新的方法来增强退化的水下图像.
- 为了提高图像质量,利用消除噪声的扩散模型.
- 为了实现高分辨率图像生成,同时保持自然细节.
主要方法:
- 利用消噪扩散模型 (DDM) 进行水下图像增强.
- 在DDM中训练了噪声估计网络.
- 使用无声扩散隐性模型 (DDIM) 加快了采样过程.
- 实施了基于补丁的方法,用于任意分辨率生成重叠平均采样.
主要成果:
- 拟议的方法与最先进的技术相比,显示出优越或可比的性能.
- 基准实验验证方法的有效性和效率.
- 突出物体检测实验证实,水下环境的干扰减少了.
结论:
- 这种基于DDM的新方法有效地增强了水下图像.
- 该方法在任意分辨率下保留了自然外观和细节.
- 这一进步有助于从水下图像中提取海洋研究的语义信息.
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