A New Generative Method for Multi-focus Image Fusion of Underwater Micro Bubbles

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 (DDPM). 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 up to a 43.9% improvement over traditional SOTA 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.