Image instance segmentation based on diffusion model improved by step noisy.

In visual tasks, image instance segmentation has always been one of the most crucial core technologies in various fields, such as autonomous driving, and medical image analysis. Currently, diffusion models have become the most advanced framework method for research in the field of images. This technology was initially used in the field of image enhancement and made significant progress at one point. In this study, the main discussion revolves around how to use algorithms to improve recognition accuracy when applying diffusion models to image instance segmentation. Here, this paper creatively proposes a Step Noisy Perception (SNP) method. During the inverse denoising process at different stages of the same instance segmentation task, a controllable relationship exists between different step sizes corresponding to different Gaussian distributions. By utilizing the information between different stages of step sizes, the accuracy of the segmentation model can be improved. This research on instance segmentation tasks is valuable. The paper conducts performance tests on instance segmentation models on the COCO and LVIS datasets and compares the segmentation results of the proposed model with those of other traditional classic segmentation methods in recent years. The experimental results show that our method has an overwhelming advantage in the recognition of small and medium-sized objects and is also competitive in recognizing large objects. Additionally, compared to the training of diffusion models, our proposed generative model shows a 2.8% improvement in accuracy, proving that this improved diffusion model method has certain research potential in instance segmentation tasks.