Motion deblurring algorithm for wind power inspection images based on Ghostnet and SE attention mechanism

IET Image ProcessingVolume 17, Issue 1 p. 291-300 ORIGINAL RESEARCHOpen Access Motion deblurring algorithm for wind power inspection images based on Ghostnet and SE attention mechanism Ruxin Gao, Ruxin Gao School of Electrical Engineering and Automation, Henan Polytechnic University, Henan, China Key Laboratory of Intelligent Detection and Control, Henan of Coal Mine Equipment, Henan, China International Joint Laboratory of Direct Drive and Control, Henan of Intelligent Equipment, Henan, China Contribution: Conceptualization, Project administration, Resources, SupervisionSearch for more papers by this authorTengfei Wang, Corresponding Author Tengfei Wang [email protected] School of Electrical Engineering and Automation, Henan Polytechnic University, Henan, China Key Laboratory of Intelligent Detection and Control, Henan of Coal Mine Equipment, Henan, China International Joint Laboratory of Direct Drive and Control, Henan of Intelligent Equipment, Henan, China Correspondence Tengfei Wang, School of Electrical Engineering and Automation, Henan Polytechnic University, Henan 454000, China Email: [email protected]Search for more papers by this author Ruxin Gao, Ruxin Gao School of Electrical Engineering and Automation, Henan Polytechnic University, Henan, China Key Laboratory of Intelligent Detection and Control, Henan of Coal Mine Equipment, Henan, China International Joint Laboratory of Direct Drive and Control, Henan of Intelligent Equipment, Henan, China Contribution: Conceptualization, Project administration, Resources, SupervisionSearch for more papers by this authorTengfei Wang, Corresponding Author Tengfei Wang [email protected] School of Electrical Engineering and Automation, Henan Polytechnic University, Henan, China Key Laboratory of Intelligent Detection and Control, Henan of Coal Mine Equipment, Henan, China International Joint Laboratory of Direct Drive and Control, Henan of Intelligent Equipment, Henan, China Correspondence Tengfei Wang, School of Electrical Engineering and Automation, Henan Polytechnic University, Henan 454000, China Email: [email protected]Search for more papers by this author First published: 19 September 2022 https://doi.org/10.1049/ipr2.12634Citations: 1AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Aiming at the problem of motion blur in the inspection image of wind power generation equipment, an improved fast motion blur removal method based on deblurganv2 is proposed in this paper. Firstly, according to the characteristics of disparate local motion blur degrees and different global motion blur directions of the wind power inspection image, the real wind power fuzzy image data set is collected and produced. Secondly, according to the characteristics of the single background of the wind power inspection image and the requirements of fast and effective processing, the lightweight network Ghostnet is redesigned as the backbone network of the Deblurganv2 generator to reduce the number of network parameters and calculations. Finally, five SE channel attention mechanism layers are added to GhostNet to strengthen feature extraction, and the upsampling process is optimized through bilinear interpolation, to improve the deblurring performance of the wind power inspection image. The experimental results show that compared with other algorithms, the proposed algorithm has a higher peak signal-to-noise ratio and structural similarity, the network model parameters are compressed to 6.1 MB, and the reasoning speed is improved to 0.42 s. 1 INTRODUCTION Wind energy is a sustainable energy source with almost no natural pollution. Wind turbines are the main equipment for converting natural wind energy into electricity. The routine maintenance of wind turbines is mainly identified by the surface damage of wind turbines showing recognizable visual features such as leading-edge corrosion, surface cracks, damaged lightning receivers, damaged vortex generators etc. The acquisition of images of wind turbine surface damage is obtained by a vehicle carrying an optical camera, enabling low-cost and frequent inspections, high-resolution optical image acquisition, and minimal human intervention, resulting in low-cost predictive maintenance. When the aircraft carries out the appearance inspection of wind power generation equipment, because the wind power generation equipment may run at different linear speeds, the aircraft also flies at a certain speed (may be affected by the wind, fast and slow, or sudden rotation), both will cause the image to produce global motion blur and local motion blur, and the wind power generation equipment rotation due to the wind blade diameter is too large, when the linear speed is certain, the closer to the edge of the wind blade its rotation. The larger the amplitude, the greater the rotation of the wind blades nearer to the edge when the wind blades are rotating with a certain linear speed, thus causing different degrees of blurring of the wind blades on the same inspection image and making it easy for fast motion blur to appear [1]. Motion blurred images can cause suspicious defects, flaws, and other problems that are not detected in time during the inspection process, which may cause significant damage to the equipment, so for the wind power inspection system, clear and reliable images are required, and the processing of inspection images should be timely and effective. Therefore, higher accuracy and faster recovery speed are required in the pre-processing of image deblurring. It is important to research a suitable method for wind power inspection image recovery. 2 RELATED WORK At present, the restoration of motion-blurred images of wind power generation mainly includes traditional algorithms based on prior knowledge and deblurring algorithms based on deep learning. The traditional method assumes that the blurred image is obtained by convolving a clear image with the corresponding blur kernel and then superimposing the noise in the transformation process. Fergus et al. [2] assumed that the blur is uniformly distributed over the entire image to eliminate camera shake blur. Shan et al. [3] used blur kernel estimation and a unified probabilistic model for blur-free image restoration to compute deblurred images. Krishman et al. [4] describe a deconvolution method that improves the slow recovery speed using hyper-Laplace priors. Levin et al. [5] studied the limitations and improvement measures of the commonly used Maximum Posterior (MAP) method from a conceptual perspective. Perrone et al. [6] extended their work on a posteriori (MAP) methods with a special focus on the total variation (TV) regularization, and although some performance improvements were achieved during development, iterative optimization methods generally suffer from some limitations. Wang Hua [7] improved the Canny operator edge extraction algorithm to solve the problem of ringing effect and loss of image detail information caused by inaccurate image edge extraction and blur kernel estimation. Li et al. [8] proposed a blind image deblurring method based on data-driven discriminative priors using a binary classifier of deep convolutional neural networks. Wen et al. [9] proposed a simplified local minimum pixel sparsity before flexibly inducing sparsity in PMP under the maximum a posteriori probability (MAP) framework. The difficulty of the traditional method is the estimation of the blur kernel. The main factor affecting the deblurring effect is also the prior estimation of the image. The method can only be used for deblurring a single image, and the deblurring effect for a large number of images is not good and can be restored very slowly. With the continuous development of deep learning technology, deep learning deblurring technology does not need to estimate the blur kernel of the image and can restore blurred images in large batches, which solves the drawbacks of traditional methods. Gong [10] and Nah [11] proposed a kernel less end-to-end method to directly deblur images without estimating blur kernels, but the algorithm mainly relies on large-capacity network models. Ramakrishnan et al. [12] combined the pix2pix framework and densely connected convolutional networks for kernel-free blind image deblurring. Kupyn et al. [13, 14] proposed DeblurGAN, and introduced the feature pyramid network into deblurring, flexibly cooperated with various backbone networks, and better preserved the texture details of the image. Zhang et al. [15] combined two GAN models, learning to blur GAN (BGAN) and learning to deblur GAN (DBGAN), to learn a better image deblurring model by mainly learning how to blur images. Zamir et al. [16] proposed a two-faceted MPRNet approach, in which information is not only sequentially exchanged from early to late, but there are also lateral connections between feature processing blocks to avoid any information loss. Although the deblurring algorithms proposed by Suin et al. [17], Zamir et al. [18], and Sung [19] respectively achieve better performance, these models require a larger amount of parameters. With the proposal of Transforme, Wang et al. [20], Lin et al. [21], and Shen et al. [22] used different methods to achieve the recovery of motion blur images respectively, and their performance was greatly improved, but their methods have the disadvantages of a large number of parameters and slow operation speed. Both traditional methods and deep learning are aimed at global motion blur, without considering local motion blur, and it is difficult to take into account the effect of image restoration and restoration speed, and it is not targeted for specific scenes and specific objects. Aiming at the causes of motion blur in the inspection image of wind power generation equipment and the situation of global motion blur and local motion blur, this paper puts forward the fuzzy data set of wind power generation equipment in the real scene, so that the designed network can learn the motion blur of the real scene of wind power equipment and solve the problem of motion blur in the patrol image of wind power generation equipment. Based on Deblurganv2 [14], this paper redesigns the generator network and uses the lightweight network GhostNet [23] to reduce the number of parameters and calculation of the network, to speed up the recovery speed of the network. The SE [24] attention mechanism is added to the Ghostnet network to strengthen feature extraction, and the upsampling process is optimized through bilinear interpolation to improve the performance of this algorithm for motion blur removal while maintaining the recovery speed. This algorithm takes wind power inspection images as the research object, and can effectively solve the global motion blur and local motion blur problems that have the same characteristics as wind power inspection images. 3 ALGORITHM DESIGN The foreground of the inspection image of wind power generation equipment is relatively simple, the colour is single (mainly white, with some coloured stripes), the structure lines are relatively straight, and the background is relatively fixed, mostly the sky (with the sun, moon, and stars, blue sky, there are all kinds of white and dark clouds), fields, mountains, rivers, houses, roads, and other natural or man-made scenes, and many images show point-like or texture characteristics. According to the above characteristics of wind power inspection image, this paper redesigns the lightweight network Ghostnet [23] as the generator network based on Deblurganv2, adds SE channel attention mechanism at the connection of ghost module of Ghost Bottleneck to strengthen feature extraction, optimizes the upsampling process through bilinear interpolation, and improves the performance of this algorithm to remove motion blur on the premise of maintaining the restoration speed, solve the problem of complex motion blur in the inspection image of wind power generation equipment. Network model compression The original network uses Mobilenet as the generator, and there is still room for a decrease in the number of parameters and computation (as in Equation 1), so this paper uses the Ghostnet network to compress the generator model. Ghosenet is the result of a linear transformation of the feature map extracted by ordinary convolution to obtain the high-bit convolution feature map. Ordinary convolutional network: set input image X ∈ R c × h × w $X \in {R}^{c \times h \times w}$ , where c is the channel of the image input, h and w are the height and width of the image input, then the generated n feature maps Y = X ⊗ f + b $$\begin{equation}Y = X \otimes f + b \end{equation}$$ (1) The above formula: Y ∈ R n × h ′ × w ′ $Y \in {R}^{n \times h^{\prime} \times w^{\prime}}$ is the output characteristic diagram, f ∈ R n × c × k × k $f \in {R}^{n \times c \times k \times k}$ is the size of the convolution kernel, ⊗ is convolution operation, and b is noise. The FLOPS required in this process is n × h ′ × w ′ × c × k × k $n \times h^{\prime} \times w^{\prime} \times c \times k \times k$ , in Ghostnet (as in Equation 2), the high-dimensional feature map is obtained by linearly transforming the low-latitude feature map volume, that is, the high-dimensional feature map with n is original to be generated, and now part of the high-dimensional feature map is obtained by the low-dimensional feature map, namely Y ′ = X ⊗ f ′ $$\begin{equation}Y^{\prime} = X \otimes f^{\prime}\end{equation}$$ (2) The above formula Y ′ ∈ R n × h ′ × w ′ $Y^{\prime} \in {R}^{n \times h^{\prime} \times w^{\prime}}$ is the output characteristic diagram, f ′ ∈ R n × c × k × k $f^{\prime} \in {R}^{n \times c \times k \times k}$ is the size of convolution kernel, to generate an n dimensional feature map, it is only necessary to perform a simple linear transformation on the feature map generated by ordinary convolution (as in Equation 3) Y j = Φ ( Y i ) j ∈ 0 , … , m i ∈ m + 1 , … , n $$\begin{equation} \def\eqcellsep{&}\begin{array}{l} {Y}_j = \Phi ({Y}_i)\\ j \in 0, \ldots ,m\\ i \in m + 1, \ldots ,n \end{array} \end{equation}$$ (3) The above formula Y j ${Y}_j$ is a feature map generated by a simple linear transformation, Y i ${Y}_i$ is a feature map generated by ordinary convolution, Φ() representing ordinary linear transformation. Assuming that the Ghotsnet module has only one inherent feature map and m · ( s − 1 ) = n s · ( s − 1 ) $m \cdot (s - 1) = \frac{n}{s} \cdot (s - 1)$ linear changes, and the size of each convolution kernel is, then the speedup of Ghostnet is (as in Equation 4). r s = n · h ′ · w ′ · c · k · k n s · h ′ · w ′ · c · k · k + ( s − 1 ) · n s · h ′ · w ′ · c · d · d = c · k · k 1 s · c · k · k + ( s − 1 ) · 1 s · d · d ≈ c · s s + c − 1 ≈ s $$\begin{equation} \def\eqcellsep{&}\begin{array}{l} {r}_s = \dfrac{{n \cdot h^{\prime} \cdot w^{\prime} \cdot c \cdot k \cdot k}}{{\dfrac{n}{s} \cdot h^{\prime} \cdot w^{\prime} \cdot c \cdot k \cdot k + (s - 1) \cdot \dfrac{n}{s} \cdot h^{\prime} \cdot w^{\prime} \cdot c \cdot d \cdot d}}\\ = \dfrac{{c \cdot k \cdot k}}{{\dfrac{1}{s} \cdot c \cdot k \cdot k + (s - 1) \cdot \dfrac{1}{s} \cdot d \cdot d}} \approx \dfrac{{c \cdot s}}{{s + c - 1}} \approx s \end{array} \end{equation}$$ (4) Theoretical compression ratio is s (as in Equation 5). r c = n · c · k · k n s · c · k · k + ( s − 1 ) · n s · d · d ≈ c · s s + c − 1 ≈ s $$\begin{equation}{r}_c = \frac{{n \cdot c \cdot k \cdot k}}{{\frac{n}{s} \cdot c \cdot k \cdot k + (s - 1) \cdot \frac{n}{s} \cdot d \cdot d}} \approx \frac{{c \cdot s}}{{s + c - 1}} \approx s\end{equation}$$ (5)where c is the number of channels of the input image, h is the height of the input image, w is the width of the input image, k is the size of the convolution kernel, and n is the number of the convolution kernel. In this paper, according to the characteristics of wind turbine blades, such as obvious defects and blade contour characteristics, relatively straight structural lines, and relatively fixed background, when the dimension of each layer changes, the compression ratio is set to 2, and that of other layers is set to 1. SE-ghost bottleneck In this paper, after using GhostNet network as the backbone network of the generator, the network parameters are greatly reduced, but its deblur performance can only approach deblurganv2 algorithm. This paper improves the Ghost Bottleneck module to improve the performance of the algorithm. Ghost Bottleneck module is a bottleneck structure designed by Ghostnet, mainly composed of Ghost module. The improvements to Ghost Bottleneck in this paper are as follows: The structure with a step size of 1 is mainly composed of two Ghost module modules in series. The first Ghost module is to expand the number of channels of the input data, and the second Ghost module is to compress the expanded channel and keep it with the input data. With the same size, this paper adds an SE attention mechanism between the two Ghost modules to strengthen the connection between the output feature maps, ss shown in Figure 1a. The network structure with a stride of two is based on the structure of (1), and a depthwise separable convolution block is inserted between the two Ghost modules to reduce the number of parameters. In this paper, the SE attention mechanism is added after the depthwise separable convolution block to strengthen the connection between output feature maps, as shown in Figure 1b. Generator network design In this paper, the generator network is redesigned according to the principle of fast and effective image processing for wind power equipment inspection. As shown in Figure 2, in this paper, the lightweight network Ghostnet is used as the backbone network of the generator. Because the wind power equipment has a simple foreground, a single colour, and relatively straight structural lines, the background is relatively fixed, mostly in natural scenes such as sky and fields, or man-made scenes, many natural object images exhibit point-like or textured properties. Therefore, this algorithm follows the FPN structure when redesigning the network. FIGURE 1Open in figure viewerPowerPoint SE-Ghost bottleneck composition. (a) SE-Ghost bottleneck with stride 1. (b) SE-Ghost bottleneck with stride 2. FIGURE 2Open in figure viewerPowerPoint The network structure diagram of the generator of this paper As shown in Figure 4, the generator inputs a blurred picture, first through a 3 × 3 convolution block, and then the feature is extracted from bottom to top through downsampling through eight ghost block structures, in which each ghost block structure is composed of several SE-ghost bottleneck modules. The data is downsampled every two ghost block feature blocks to extract more semantic features. The output feature map of each layer is first 1 × 1 to align the dimensions and stack with the feature map obtained by bottom-up upsampling to supplement more detailed features. In the upsampling process, bilinear interpolation is used to replace the nearest-neighbour interpolation method to enlarge the image with the sampling ratio of 8, 4, 2, and 1 in the feature map. The image is smoother when zoomed in to solve the checkerboard artefacts in the restored image. The main parameters of its generator network are shown in Table 1. TABLE 1. Main parameters of a generator network Input Conv block Output Stride SE S 224 × 224 × 3 $224 \times 224 \times 3$ Conv3×3 16 2 1 112 × 112 × 16 $112 \times 112 \times 16$ Ghost module 16 1 2 112 × 112 × 16 $112 \times 112 \times 16$ Ghost module 24 2 1 1 56 × 56 × 24 $56 \times 56 \times 24$ Ghost module 24 1 2 56 × 56 × 24 $56 \times 56 \times 24$ Ghost module 40 2 1 1 28 × 28 × 40 $28 \times 28 \times 40$ Ghost module 40 1 2 28 × 28 × 40 $28 \times 28 \times 40$ Ghost module 80 2 1 1 14 × 14 × 80 $14 \times 14 \times 80$ Ghost module 80 1 1 14 × 14 × 80 $14 \times 14 \times 80$ Ghost module 80 1 1 14 × 14 × 80 $14 \times 14 \times 80$ Ghost module 80 1 2 14 × 14 × 80 $14 \times 14 \times 80$ Ghost module 112 1 1 1 14 × 14 × 112 $14 \times 14 \times 112$ Ghost module 112 1 2 14 × 14 × 112 $14 \times 14 \times 112$ Ghost module 160 2 1 1 7 × 7 × 160 $7 \times 7 \times 160$ Ghost module 160 1 1 7 × 7 × 160 $7 \times 7 \times 160$ Ghost module 160 1 1 7 × 7 × 160 $7 \times 7 \times 160$ Ghost module 160 1 1 7 × 7 × 160 $7 \times 7 \times 160$ Ghost module 160 1 4 EXPERIMENTS Dataset production Motion blur of wind power inspection images is composed of global motion blur and local motion blur. In a global motion blurred image, all regions of the image are blurred, usually caused by a camera shake [25]. Local motion blur is caused by the mismatch between the wind blades and the speed of the aircraft. Existing deblurring datasets [26] are mainly constructed for global deblurring, using methods that synthesize blurred images by convolving uniform or non-uniform blur kernels with sharp images [27, 28] as well as shaking the camera while recording to simulate blur caused by camera rotation, and averaging consecutive short exposure frames to generate a globally blurred image [29]. The establishment of local motion blur data set has received little attention. According to the characteristics of the Wind Power Patrol image and the dynamic range of each frame [30], this paper establishes the local blur image data set by fixing the camera, rotating the average continuous frame of the wind blade as the blurred image [31]. Because this algorithm is supervised learning, clear and fuzzy image pairs are required during training. This paper aims to make the algorithm better applicable to the scene of wind power plant inspection, and the data set can contain the fuzzy problems in a variety of situations such as global motion blur and local motion blur. A method of selecting the centre frame as the clear image by using the fixed camera, rotating the average continuous frame of the wind blade as the blurred image when making the dataset, and a method of convoluting a uniform or non-uniform blur kernel with a clear image, the fuzzy image dataset with wind power equipment as the specific object is made, so that the deep network can learn a variety of fuzzy problems of Wind Power Patrol image. In this paper, three datasets are made: Subsets A, Subsets B, and Subsets C. Subsets A: Using convolution operation, convolute the clear images with the same blur intensity and different blur cores, different motion blur intensity and the same blur check to obtain the blurred images. A total of 6000 pairs of training dataset are produced. The test dataset is 1000 pairs. As shown in Figure 3, the size of the convolution kernel is, and the motion blur intensity is 0 to 1 to obtain a blurred image. This dataset can fully simulate various global motion blurs that appear in wind power inspection images. Subsets B: In this paper, a small wind turbine with a height of 2.5 m and a diameter of 1.6 m is used to build the dataset. Use 60 frame rate camera to shoot the wind power equipment, divide the video into frames, expand the 60 frame rate video to 240 frame rate by interpolation method, and take the average value of the image every 15 frames as the blurred image, to realize the blur in the real scene. As shown in Figure 4, the composite image can appear local blur due to different linear speeds of wind power equipment, as well as a fast-motion blur at the edge of the wind blade. In this paper, 3600 pairs of training dataset and 600 pairs of test dataset are produced. This dataset can fully simulate the local motion blur and fast motion blur of wind power generation equipment. Subsets C: It is composed of dataset A and dataset B, including 6000 pairs of training dataset and 1000 pairs of test dataset. This dataset contains the motion blur phenomenon in various situations of Wind Power Patrol image. FIGURE 3Open in figure viewerPowerPoint Subsets A blurred images. Clear images obtained by video framing. (a) Strength 0, (b) Strength 0.25, (c) Strength 0.5, (d) Strength 0.75, (e) Strength 1.0. The blurred image is obtained by the convolution kernel size of 100 × 100 with different motion intensities. FIGURE 4Open in figure viewerPowerPoint Comparison of synthetic images of Subsets B and real blurred image. (a) Synthetic blurred image. (b) Real blurred image. Experimental data preprocessing and experimental configuration In this paper, the training dataset is enhanced by turning left and right, random affine transformation, transpose, non-rigid body transformation, random change of image colour, saturation and value, random brightness, contrast, and random erasure, to strengthen the generalization ability of the network. The experimental platform in this paper is i7-10700 CPU frequency 3.6G, Nvidia 3080 GPU, Windons10 operating system, and PyTorch deep learning framework. Hyperparameter settings in this paper: instance is used for regularization, Adam is used for generator optimizer, and SGD is used for discriminator optimizer. Analysis of experimental results To verify the effectiveness of the method proposed in this paper, this paper uses the method in this paper, the method of Nah [10], Deblurganv2 [14], DBgan [15], PMP [9], and the Ghostnet method that only replaces the backbone network to process the proposed wind power equipment test dataset, compares the processing results with the subjective visual effects, compares and analyzes the PSNR and SSIM objective indicators, and counts the speed of restoring a single image and the parameters of the generated model. 4.3.1 Comparison test of the subjective effect of restored pictures Natural pictures (blurred pictures from real scenes) taken with industrial cameras are deblurred and the defects and edge contours of the recovered pictures are partially enlarged for comparative analysis of subjective effects. In order to verify the superiority of this algorithm, this paper uses the camera to take real motion blurred pictures to compare the subjective effects of the restored pictures. This paper takes three of the images for comparative analysis. Among them, Figures 5 and 6 show the global motion blur image and Figure 7 shows the local motion blur image. FIGURE 5Open in figure viewerPowerPoint Deblurring results of various methods for image 1 and its partial magnification. (a) Blurred image, (b) Nah, (c) Deblurgan v2, (d) DBgan, (e) PMP, (f) Ghostnet, (g) Our. FIGURE 6Open in figure viewerPowerPoint Deblurring results of various methods for image 2 and its partial magnification. (a) Blurred image, (b) Nah, (c) Deblurgan v2, (d) DBgan, (e) PMP, (f) Ghostnet, (g) our. FIGURE 7Open in figure viewerPowerPoint Deblurring results of various methods for image 3. (a) Blurred image, (b) Nah, (c) Deblurgan v2, (d) DBgan, (e) PMP, (f) Ghostnet, (g) our. Comparing the experimental results of three images in Figures 5, 6, and 7, it can be seen that the recovery effect of the Nah method is poor; the recovery effect of the Deblurganv2 method and the DBgan method is better, but the edge contour is blurred; the recovery image of the PMP method can observe the crack of wind blade, but the whole image appears serious texture and artefact phenomenon; the recovery effect of Ghostnet method is close to that of the original network Deblurganv2, but there is a certain light halo phenomenon. The effect of Ghostnet method is close to that of the original network Deblurganv2, but there is a certain halo phenomenon; the method of this paper has a higher degree of recovery for the image, and the edge features are clearer. In Figure 5, this method, Ghostnet method, Deblurganv2 method, and DBgan method all remove the motion blur of the image, but the contour features are clearer after this method. In Figure 6, the motion blur is removed in this paper, Ghostnet method, DBgan method, and Deblurganv2 method, but the Ghostnet method and Deblurganv2 method have cracked and scattered contour edges and the image has checkerboard artefacts, while this paper optimizes the upsampling process to make the contour features more obvious and eliminate checkerboard artefacts. In Figure 7, this method solves the problem of greater blurring the closer the wind blade edge due to the same angular velocity and different linear velocities, as well as the problem of fast motion blurring at the edge of the wind blade, and basically recovers all the edge contours of the wind blade; the other methods recover the edge contours of the wind blade partially and do not solve the problem of fast motion blurring at the wind blade edge. Through the above comparison, the method in this paper solves the global motion blur and local motion blur problems that appear in the wind power equipment inspection images. 4.3.2 Objective evaluation of full-reference image quality, parameter quantity, and restoration speed (1) On the fuzzy image test dataset proposed in this paper, the