Motion blur image restoration algorithm for wind power equipment based on Fourier convolution and HBN mechanism

Abstract

To address the motion blur problem in inspection images of wind power equipment, this paper proposes a fast motion deblurring method based on a Multi-Input Multi-Output (MIMO) framework. First, considering the presence of both linear and nonlinear motion blur in wind power equipment images, we construct a real-world motion-blur dataset for wind turbine inspection. Second, to capture the frequency characteristics of blurred inspection images, a Fourier domain convolution model was designed, enabling the network to better capture global differences between blurred and sharp images. This improves performance while reducing model size. Then, a Half-Batch Normalization (HBN) module is proposed to retain more original feature information during normalization, further improving the algorithm's effectiveness. Additionally, an Efficient Channel Attention (ECA) mechanism is integrated into the network to expand the receptive field of convolution operations and enhance the performance of Fourier convolution, thereby improving deblurring quality. Experimental results demonstrate that the proposed method outperforms existing algorithms in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on wind power inspection images. Furthermore, the model size is compressed to 39.5 MB, and the restoration speed is increased to 0.52 s. The code is available at : https://github.com/lingzhiy/Motion.