DBD-Diff: Defocus Blur Detection Using Semantic and Texture Correlation Guided Diffusion Model

Defocus blur detection (DBD) is essential in computer vision as it facilitates the precise identification and detection of in-focus regions within images. However, recent methods may cause accuracy degradation where the foreground and background objects are highly similar or have small color differences. Diffusion models have shown their robust performance in various computer vision tasks, as they can efficiently reconstruct images from noise features and naturally incorporate multiple object features through each iterative generation process. However, diffusion models for Defocus Blur Detection (DBD) tasks have not yet been explored, which may hinder advancements in DBD fields. In this work, we propose a novel diffusion framework specifically tailored for Defocus Blur Detection (DBD) tasks. To the best of our knowledge, this is the first diffusion model specifically designed for DBD. Then a Cross Domain Correlation Extractor and a Cross Domain Tokenized KAN are further proposed to extract two groups of object features, that can generate the semantic correlation feature and the texture correlation feature between the foreground and the background object for the diffusion model. Finally, we conduct extensive experiments to evaluate our framework on several datasets, and the results show that our method achieves state-of-the-art segmentation performance.