Li Xionghui, Zong Siguang, Duan Zike, Yang Shaopeng, Chen Bao, Lin Qiqin
College of Electronic Engineering, Naval University of Engineering, Wuhan, 430033, China.
Zhuhai Technician College, Zhuhai, 519000, China.
Sci Rep. 2024 Dec 5;14(1):30280. doi: 10.1038/s41598-024-80028-7.
The optical detection methodology stands as a predominant approach for detecting underwater bubbles. Nonetheless, owing to poor underwater imaging conditions, the acquired image depth of field proves inadequate, posing significant challenges for the study and identification of underwater micro bubbles. In this investigation, we present a multi-focus image fusion model tailored for underwater micro bubbles, grounded in the Denoising Diffusion Probabilistic Model. We also propose a multi-focus image fusion metric suitable for underwater scenarios with micro bubbles. Experimental validation on the constructed dataset demonstrates that our model achieves better results than traditional methods. These results substantiate the model's efficacy in conserving image characteristics and attaining multi-focus fusion. Consequently, this research furnishes substantial empirical support for subsequent endeavors in image-related tasks.
光学检测方法是检测水下气泡的主要方法。然而，由于水下成像条件较差，所获取图像的景深不足，给水下微气泡的研究和识别带来了重大挑战。在本研究中，我们基于去噪扩散概率模型，提出了一种针对水下微气泡的多聚焦图像融合模型。我们还提出了一种适用于含微气泡水下场景的多聚焦图像融合度量。在构建的数据集上进行的实验验证表明，我们的模型比传统方法取得了更好的结果。这些结果证实了该模型在保留图像特征和实现多聚焦融合方面的有效性。因此，本研究为后续图像相关任务的研究提供了大量的实证支持。
