Wind turbine equipment motion blur image restoration algorithm optimized by diffusion model denoising diffusion probabilistic model strategy

In the inspection system for wind power generation, ensuring the clarity and reliability of captured images is of paramount importance. However, factors such as camera shake, equipment vibration, and the high-speed rotation of wind turbine blades can result in blurred images. Failure to promptly detect defects or flaws in such blurred images may lead to severe equipment damage, posing significant safety risks and causing substantial economic losses. We propose a solution algorithm for image blur issues caused by the motion of wind turbine equipment: a wind turbine blur image restoration algorithm based on denoising diffusion probabilistic models (DDPMs). This algorithm incorporates the DDPM model into the field of image deblurring, aiming to enhance the stability and reliability of the training process, effectively avoiding the problem of mode collapse. Furthermore, an iterative approach is adopted for image restoration, progressively enhancing the details and quality of the image through multiple operations, thereby better capturing subtle textures and details in the image. Lastly, a multi-scale feature fusion mechanism is introduced. In each iteration, by fusing features from different scales, the model comprehensively understands and reconstructs the image, thereby improving the effectiveness of image restoration. Experimental results show that the proposed algorithm achieves a peak signal-to-noise ratio (PSNR) of 31.4957 and structural similarity index measurement (SSIM) of 0.9297 on wind turbine blur image dataset A and a PSNR of 51.5231 and SSIM of 0.9944 on dataset B. Compared with other algorithms, the experimental results demonstrate superior performance.