Rule-Semantic Generative Calibration Blur Detection for UAV Imagery

Blur detection is a novel method for evaluating image quality in the context of Unmanned Aerial Vehicle (UAV) surveillance and monitoring activities. However, existing methods lack multi-scenario adaptability due to the overreliance of deep learning models on learned priors from limited datasets, reducing their adaptability to unfamiliar conditions. To address this issue, the Denoising Diffusion Implicit Model (DDIM) is integrated with a paradigm named Rule-based Semantic Calibration (RSC) to create Rule-Semantic Generative Calibration Blur Detection (RSGC-BD). This approach generates robust blur detection maps through an iterative calibration process that enhances generalization capabilities. Unlike current Blur Detection (BD) methods, which categorize pixels as blurred or unblurred with a single forward propagation, the suggested approach employs the DDIM-based generative model to create and refine a BD map iteratively. By utilizing the iterative calibration process through RSC to integrate rule-based blur masks into generative semantic results at each step, this model ensures high-precision blur prediction, enhanced multi-scenario adaptability, and significantly improved inference speed. Furthermore, we propose a conversion module, namely the Adaptive RGB-to-Grayscale Conversion Cascade (ARGC-Cascade), to convert RGB images to grayscale through adaptive integration, highlighting blurred regions and improving detection accuracy. This enhancement of blur features is achieved by balancing the spectral channel weights during image conversion. The superior performance of the proposed RSGC-BD approach is validated by extensive tests on four high-resolution BD datasets, including the newly introduced UAV-BD. Source codes are available at: https://github.com/udrs/RSGCBD.