Multi‐scale single image dehazing based on the fusion of global and local features

IET Image ProcessingVolume 16, Issue 8 p. 2049-2062 ORIGINAL RESEARCHOpen Access Multi-scale single image dehazing based on the fusion of global and local features Ziyu Chen, Ziyu Chen orcid.org/0000-0001-6422-5792 School of Information and Communication Engineering, University Of Hainan, Hainan, ChinaSearch for more papers by this authorHuaiyu Zhuang, Huaiyu Zhuang China Mobile Financial Technology Co., Ltd., Beijing, ChinaSearch for more papers by this authorJia Han, Jia Han 18 Dahuisi Road, Haidian District, Beijing, ChinaSearch for more papers by this authorYani Cui, Corresponding Author Yani Cui [email protected] orcid.org/0000-0002-8828-9966 School of Information and Communication Engineering, University Of Hainan, Hainan, China Correspondence Yani Cui, School of Information and Communication Engineering, University Of Hainan, Hainan 570228, China. Email: [email protected]Search for more papers by this authorJiaxian Deng, Jiaxian Deng School of Information and Communication Engineering, University Of Hainan, Hainan, ChinaSearch for more papers by this author Ziyu Chen, Ziyu Chen orcid.org/0000-0001-6422-5792 School of Information and Communication Engineering, University Of Hainan, Hainan, ChinaSearch for more papers by this authorHuaiyu Zhuang, Huaiyu Zhuang China Mobile Financial Technology Co., Ltd., Beijing, ChinaSearch for more papers by this authorJia Han, Jia Han 18 Dahuisi Road, Haidian District, Beijing, ChinaSearch for more papers by this authorYani Cui, Corresponding Author Yani Cui [email protected] orcid.org/0000-0002-8828-9966 School of Information and Communication Engineering, University Of Hainan, Hainan, China Correspondence Yani Cui, School of Information and Communication Engineering, University Of Hainan, Hainan 570228, China. Email: [email protected]Search for more papers by this authorJiaxian Deng, Jiaxian Deng School of Information and Communication Engineering, University Of Hainan, Hainan, ChinaSearch for more papers by this author First published: 13 March 2022 https://doi.org/10.1049/ipr2.12467AboutSectionsPDF 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 problems of incomplete dehazing of a single image and unnaturalness of the restored image, a multi-scale single-image defogging network with local features fused with global features is proposed, using fog and non-fogging image pairs train the network in a direct end-to-end manner. The network is divided into global feature extraction module, multi-scale feature extraction module and deep fusion module. The global feature extraction module extracts global features that characterize the contour; multi-scale feature extraction module extracts features at different scales to improve learning accuracy; in the deep fusion module, the convolutional layer extracts the local features that describe the image content, and then the local features and the global features are merged through skip connections. Comparative experiments were carried out on artificially synthesized fog images and real fog images. The experimental results show that the algorithm proposed here can achieve the ideal dehazing effect, and is superior to other comparison algorithms in subjective and objective aspects. 1 INTRODUCTION In foggy weather, a large number of suspended particles in the air will scatter the light [1]. Images taken in outdoor scenes usually have poor visibility, low contrast, and blurred image quality, which seriously affect the visual effect [2]. Therefore, image dehazing has important research significance. The current image dehazing methods are divided into traditional image dehazing methods and methods based on deep learning. Traditional dehazing methods are divided into methods based on image enhancement and methods based on physical models [3]. Based on the image enhancement method, the purpose is to improve the contrast of the image, so that the image has a better visual effect. Such as histogram equalization, Retinex contrast enhancement, wavelet transform algorithm etc. However, this type of method only emphasizes improving the contrast of blurred images, and does not consider the mechanism of image degradation, so the dehazing effect is general [4]. The method based on physical model constructs an image scattering model by studying the scattering effect of suspended particles in the atmosphere on light [5-7], and uses different priori hypotheses [8] to solve the unknown parameters in the image scattering model, thereby recovering the clear haze-free image. This method takes into account the image degradation mechanism, and the dehazing effect is real and natural. He et al. [8] put forward the dark channel a priori hypothesis, from which the transmission map was obtained and optimized by guided filtering [9], and finally combined with the atmospheric scattering model to reconstruct the haze-free image. Based on the DCP method, Meng et al. [10] proposed a dehazing method that bounds the transmittance and regularizes the context. Berman et al. [11] estimated the transmission map based on a non-local prior and optimized it with a regularization method, and finally the fog-free image is restored through the atmospheric scattering model. Zhu et al. [12] proposed that the colour attenuation prior to recover the depth information to obtain the transmittance, and then combined with the atmospheric scattering model to reconstruct the haze-free image. Zhang et al. [13] first estimated the atmospheric light based on the super-pixel atmospheric light value estimation method, and proposed a new bright channel prior model to process the bright area and fuse it with the dark channel prior model, and finally used the saliency analysis method to refine the transmittance. Although dehazing methods based on physical models have made great progress, such methods are prone to colour distortion due to their dependence on various prior conditions [14]. At present, methods based on deep learning have achieved good results in the field of dehazing. Cai et al. [15] proposed DehazeNet to estimate the transmission map, and then restored the haze-free image through the atmospheric scattering model. Ren and others [16] proposed a multi-scale neural network to solve the problem of single image deblurring. The coarse-scale network estimates the corresponding transmission map of the hazy image, and then uses the localized fine-scale network to refine the transmission map. Li et al. [17] constructed a new atmospheric scattering model, integrating atmospheric light and transmittance into a new variable K, and designed AOD-Net to estimate this variable, and finally combined with the new atmospheric scattering model to restore the fog-free image. Chen et al. [18] proposed a principled synthesis to real defogging (PSD) framework, using real outdoor images to fine-tune the model trained on artificially synthesized data to improve the generalization performance of defogging. Zhang and others [19] proposed a densely connected pyramid dehazing network (DCPDN), which embeds the atmospheric scattering model into the network. The sub-network estimated the atmospheric light and transmittance and then combined the atmospheric scattering model to obtain the processed image, and finally the processed image and the original haze-free image are input to the generative confrontation network to obtain the final dehazing image. Since the above methods are all indirect end-to-end methods, they are to learn the transmittance and then reconstruct the fog-free image through the atmospheric scattering model, but the estimated transmittance often has errors, making the final dehazing image prone to colour distortion. The direct end-to-end defogging network can solve this problem, and the direct end-to-end defogging network directly learns the mapping relationship between hazy images and original haze-free images. Yang and others [14] proposed a defogging network based on the fusion of content features and style features. This method designed two independent network modules to extract the content and style information of the image, and the restored image was clear and natural. Qu et al. [20] proposed an enhanced Pix2pix dehazing network EPDN (EPDN contains a multi-resolution generator, a multi-scale discriminator and an enhancer) using a discriminator to supervise the generator on a coarse scale to generate real images, and then the intensifier generates a true fog-free image from the result of the generator on a fine scale. Qin et al. [21] proposed an end-to-end feature fusion attention network. The network is deep and can effectively extract deep feature information. However, the fog-related features obtained by the network are single, and the obtained structural attributes are not rich enough. The effect is not ideal. Chen and others [22] proposed an image dehazing algorithm based on HSI colour space, which converts RGB colour space to HSI colour space and directly learns the mapping relationship between foggy and non-fog images on colour, saturation and brightness components. However, the network structure is single, and the restored image details are insufficient. Although the current direct end-to-end dehazing network can avoid the problem of colour distortion in the image after dehazing due to inaccurate estimation of the transmission map in the indirect end-to-end network, there are still problems that the feature information extracted by the shallow network is easy to be lost in the deep network, and the extracted relevant features are single, which leads to insufficient image detail information recovery. In response to the above problems, we propose a direct end-to-end local feature and global feature fusion multi-scale network image dehazing. The method does not rely on the atmospheric scattering model. It uses hazy and original haze-free image pairs to train the network to maximize predict the visual similarity of the foggy image and the non-fog image to dehaze the image. This method uses convolutional neural network (CNN) to design global feature extraction module, multi-scale feature extraction and deep fusion module. Multi-scale convolution can extract feature information at different scales to improve learning accuracy. In the deep fusion module, local features are fused with global features through skip connections. Experiments show that the method can effectively extract image features, and the restored image colour is true and natural, and the detailed information is rich. 2 DESIGN OF NETWORK MODEL The network in this paper is divided into global feature extraction module, multi-scale feature extraction module and deep fusion module. The global feature extraction module extracts the global features that describe the edge contour of the image; the multi-scale feature extraction module extracts image features at different scales to obtain richer structural attributes; In the deep fusion module, the convolutional layer extracts the local features that describe the image content, and then the local features and the global features are merged through skip connections to prevent the global features from being lost in the deep network and speed up the network convergence. Finally, the image is restored through a 1 × 1 convolution layer. Since the colour of the real outdoor foggy image is generally dark, the gamma transform is used for brightness correction at last. The network structure proposed in this paper is shown in Figure 1 and Table 1. FIGURE 1Open in figure viewerPowerPoint The proposed network structure TABLE 1. Network structure Layer Filters Kernel Size Strides Output Shape Parameters input_1 - - - (None,64,64,3) 0 conv_top 8 (3,3) (1,1) (None,64,64,8) 224 pool_top - (2,2) (2,2) (None,32,32,8) 0 conv_2 16 (3,3) (1,1) (None,32,32,16) 1168 pool_2 - (2,2) (2,2) (None,16,16,16) 0 conv_m1 32 (1,1) (1,1) (None,16,16,32) 544 conv_m2 32 (3,3) (1,1) (None,16,16,32) 4640 conv_m3 32 (5,5) (1,1) (None,16,16,32) 12832 con_m - - - (None,16,16,96) 0 conv_4 32 (3,3) (1,1) (None,16,16,32) 27680 conv_5 32 (3,3) (1,1) (None,16,16,32) 9248 con_5 - - - (None,16,16,64) 0 conv_6 32 (3,3) (1,1) (None,16,16,32) 18464 conv_7 16 (3,3) (1,1) (None,16,16,16) 4624 up_7 - (2,2) - (None,32,32,16) 0 con_7 - - - (None,32,32,32) 0 conv_8 8 (3,3) (1,1) (None,32,32,8) 2312 up_8 - (2,2) - (None,64,64,8) 0 con_8 - - - (None,64,64,16) 0 conv_end 3 (1,1) (1,1) (None,64,64,3) 51 Total 81787 Trainable 81787 Non-trainable 0 Global feature extraction module In order to effectively extract the global features that characterize the contour information, the global feature extraction module is proposed, which consists of two 3 × 3 convolution layers and two max Pooling layers. The number of convolution kernels are 10 and 20 respectively; the step size is set to 1 and the step size of pooling layer is 2. Compared with the large convolution kernel, the 3 × 3 convolution kernel can better extract edge information and reduce the amount of calculation. The calculation formula for convolution operation is as follows: Z l i = W l i ∗ X \begin{equation}{Z_l}_i = {W_{li}} * X\end{equation} (1) In the formula, the subscript l represents the l-th convolution layer, and i represents the l-th convolution kernel in each layer, Z l i ${Z_{li}}$ represents the l-th feature output by the l-th layer, W represents the weight of the convolution kernel, X $X$ is the input mage, ∗ $ * $ represents convolution operation. Each convolution layer is followed by a max Pooling layer to halve the size and compress the number of parameters. Selecting the ReLU function [23] as the activation function of the entire network. Except for the max pooling layer, the up-sampling layer and the last output layer, the end of each convolution layer is connected to the activation function. In order to prevent the image boundary information from being lost during convolution, while keeping the input and output of the convolution at the same size, it is necessary to perform pixel filling at the edge of the image when performing the convolution calculation. The current image edge pixel filling method mainly includes two methods: zeros padding and mirror padding. Mirror padding is to fill the boundary that needs to be expanded with the value of the row or column symmetrical to the original boundary. The principle of mirror padding of the upper and lower boundaries is shown in Figure 2, the left and right borders are mirrored and filled in the same way. Figure 3 shows the result of all zero filling and mirror filling for the same image. It can be seen from the left image of Figure 3 that when all zeros are used for padding, a white aperture appears on the edge of the image block after dehazing, and the complete image has obvious splicing borders. The mirror padding method can eliminate the splicing boundary, making the processed image more real and natural, as shown in the right picture of Figure 3. Therefore, except for the last layer, every other convolutional layer is calculated with mirror filling. FIGURE 2Open in figure viewerPowerPoint Principle of mirror padding of upper and lower boundaries FIGURE 3Open in figure viewerPowerPoint The processed image (left: mirror padding, right: all zero padding) Multi-scale feature extraction module This module uses 1 × 1, 3 × 3, and 5 × 5 convolution kernels to extract features in parallel, and the specific multi-scale convolution kernel parameters are shown in Table 2. TABLE 2. Network structure Filter size Filter number Pad 1 × 1 32 0 3 × 3 32 1 5 × 5 32 2 The 1 × 1 convolution can retain more detailed features and improve the learning accuracy. Compared with the large convolution kernel, the 3 × 3 convolution kernel can better capture the edge information, with fewer parameters, and can reduce the computational complexity; and finally use 5 × 5 convolution kernel to increase the receptive field. The number of feature map channels extracted after each group of convolution operation is 32, and the feature maps after convolution of different scales are connected according to the channel. Compared with a single specification convolution, multi-scale convolution can better learn the characteristics of haze, and improve the robustness of the network at different resolutions. The captured feature map retains more details of the input and has a better dehazing effect. The multi-scale fusion features are convolved with conv_4 and then skip-connected with the output of conv_5, which speeds up the network training speed, and the stacked feature maps contain more content information. Deep fusion module The step size of the two 3 × 3 convolution layers in this module is set to 1, and the number of convolution kernels is gradually halved with the number of network layers. The output features of the conv_top and conv_2 of the global feature extraction module are connected with the local features output by the upsampling layers up_7 and up_8 of the deep network respectively according to the channel to realize the fusion of global features and local features, avoiding the loss of information in the deep network and gradient disappearance, helping the back propagation of the gradient and ensuring the effective transmission of information. The last layer uses a 3 × 3 convolution kernel to output a clear colour image. Network structure verification In order to verify the effectiveness of each module in the network proposed in this paper, the following comparison experiment is selected: (a) only multi-scale module is removed from the network. (b) Only skip connections are removed from the network. (c) Remove multi-scale and skip connections in the network. (d) The complete network mentioned in this article. During network training, the setting parameters remain unchanged. The verification result is shown in Figure 4. FIGURE 4Open in figure viewerPowerPoint Verification result. (a) Image processed by the model with only multi-scale module removed; (b) Image processed by the model with only skip connections removed; (c) Image processed by the model with multi-scale modules and skip connections removed; (d) Image processed by the complete network mentioned in this article It can be seen from the comparison of the yellow frame that in Figure 4a, there are still parts of the mist that have not been completely removed, and the dehaze is not complete. In Figure 4b and c, it can be clearly seen that the image details are blurred and the edge contours are not clear. Since no skip connection was added in experiment (c), it is easy to cause the problem of gradient disappearance in network training. On this basis, adding a multi-scale module reduces the learning efficiency. Therefore, the defogging effect of Figure 4b is worse than Figure 4c. Figure 4d adds multi-scale convolution on the basis of Figure 4a, and it can be seen that the edge detail information of the door, grass and wall in Figure 4d is clearer, and the dehazing effect is better. Multi-scale convolution divides the input features into different groups, performs independent convolution calculations and then merges them, so that the network can better learn the features, obtain more fog-related features and improve the robustness at different resolutions. In order to further verify the effectiveness of the method in this paper, the average gradient (AG) and root mean squared error (RMSE) are used for comparison and verification. The calculation formulas of the AG and RMSE are as follows: A G = 1 ( m − 1 ) · ( n − 1 ) ∑ x = 1 m − 1 ∑ y = 1 n − 1 ∂ f ∂ x 2 + ∂ f ∂ y 2 2 \begin{equation}AG = \frac{1}{{(m - 1) \cdot (n - 1)}}\sum_{x = 1}^{m - 1} {\sum_{y = 1}^{n - 1} {\sqrt {\frac{{{{\left( {\frac{{\partial f}}{{\partial x}}} \right)}^2} + {{\left( {\frac{{\partial f}}{{\partial y}}} \right)}^2}}}{2}} } } \end{equation} (2) In the above formula, m represents the row of the image, n represents the column of the image. ∂ f ∂ x $\frac{{\partial f}}{{\partial x}}$ represents the gradient in the horizontal direction, ∂ f ∂ y $\frac{{\partial f}}{{\partial y}}$ represents the gradient in the vertical direction. The AG reflects the sharpness of the image to a certain extent, the higher the value, the clearer the image. R M S E = ∑ i = 1 N p i − a i 2 N \begin{equation}RMSE = \sqrt {\frac{{\sum_{i = 1}^N {{{\left( {{p_i} - {a_i}} \right)}^2}} }}{N}} \end{equation} (3) In the formula, N is the total number of samples, i represents the i-th group of images, p is the dehazed image, a is the clear haze-free image. The RMSE reflects the error between the dehazed image and the original haze-free image, the smaller the value, indicating the processed image closer to the original image. It can be seen from Table 3 that the quality of the haze-free image recovered by the dehazing network mentioned in this paper is higher. TABLE 3. Comparison results of different network structures Evaluation indicators Experiment (a) Experiment (b) Experiment (c) Method of this article AG 6.9029 4.7211 5.2001 7.6391 RMS 0.0458 0.0734 0.0754 0.0424 The visible edge [24] is used to verify the edge detail retention of the image processed by the network, as shown in Figure 5. FIGURE 5Open in figure viewerPowerPoint (a) Number of visible edges. (b) Number of visible edges The left pictures of Figure 5a and b are the visible edge images of the foggy figure. The right image of Figure 5a is the visible edge figure of the image processed by the network without multi-scale modules and skip connections, and the right image of Figure 5b is the visible edge figure of the image processed by the network we proposed. It can be seen from Figure 5 that the foggy image has the lowest number of visible edges, and the image processed by the network without multi-scale and skip connection module can only restore part of the edge information. The image processed by the proposed network has the highest number of visible edges, which can better restore the edge and detail information of the original haze-free image, further verifying the effectiveness of the modules in the proposed network. Loss function In order to train the network effectively, the mean square error (MSE) is used as the loss function to estimate the error between the dehazed image and the clear image. Its expression is as follows: M S E = 1 N ∑ i = 1 N p i − a i \begin{equation}MSE = \frac{1}{N}\sum_{i = 1}^N {\left\| {\left( {{p_i} - {a_i}} \right)} \right\|} \end{equation} (4) The smaller the MSE, the closer the learned data is to the true value, and the dehazing image is closer to the original clear image. In this paper, stochastic gradient descent (SGD) is used to train the network parameters in reverse to minimize the loss function MSE. The network parameters are updated from the last layer to the upper layer during network parameter training. The process is as follows: W l − 1 = W l − β · ∂ J W l \begin{equation}W^{l - 1} = W^l - \beta \cdot \partial \frac{J}{{W^l}}\end{equation} (5) b l − 1 = b l − β · ∂ J b l \begin{equation}b^{l - 1} = b^l - \beta \cdot \partial \frac{J}{{b^l}}\end{equation} (6) Among them W represents the weight of the convolution kernel, l represents the l-th convolution layer, β $\beta $ is the learning rate, b $b$ is the bias and J is the MSE. 3 EXPERIMENTAL RESULTS AND ANALYSIS Experimental data and settings In this paper, some clear indoor and outdoor fog-free images are selected from Middlebury Stereo Datasets [25], and fogged image sets are synthesized by artificial fogging through the physical model of atmospheric scattering. Each hazy image and haze-free image was cut into a size of 64 × 64, from which 9000 image blocks were selected as the experimental training samples and labels, and finally the test sample blocks were spliced into a complete image. The experiment was carried out under Windows 10 system, and the experimental hardware configuration was Intel Core i7-9750 [email protected] GHz 16GB RAM, NVDIA GeForce GTX 1650. The TensorFlow deep learning framework is used to train the network, the weights of each layer are initialized randomly, and the Adam optimizer is used to optimize the network. The learning rate is set to 0.001, the batchsize is set to 64, and the number of iterations is 1000. This paper conducts experiments from two aspects of synthetic hazy image and real hazy image. The method of BCCR (Meng et al. [10]), NLID (Berman et al. [11]), DCP (He et al. [9]), DehazeNet (Cai et al. [15]), FFA-Net (Qin et al. [21]), SOTSFDE (Ling et al. [27]), MBE (Cho et al. [28]) and PSD (Chen et al. [18]) were selected to conduct a comparative test from both subjective and objective aspects. Comparative analysis of synthetic hazy images First of all, experiments are conducted on the synthetic data set. In order to verify the effectiveness of the method in this paper, the experiment compares the outdoor image and the indoor image from both subjective and objective aspects. Three indoor images and three outdoor images are randomly selected from Middlebury Stereo Datasets to artificially add fog to get the foggy image, and compare and analyse with other algorithms. Figures 6-8 show the comparison of the dehazing results of the synthesized foggy images. FIGURE 6Open in figure viewerPowerPoint From left to right are the results of (a) Hazy image; (b) BCCR; (c) NLID; (d) DCP; (e) DehazeNet; (f) FFA-Net; (g) MBE; (h) PSD; (i) SOTSFDE; (j) Ours; (k) Haze-free image FIGURE 7Open in figure viewerPowerPoint From left to right are the results of (a) Hazy image; (b) BCCR; (c) NLID; (d) DCP; (e) DehazeNet; (f) FFA-Net; (g) MBE; (h) PSD; (i) SOTSFDE; (j) Ours; (k) Haze-free image FIGURE 8Open in figure viewerPowerPoint From left to right are the results of (a) Hazy image; (b) BCCR; (c) NLID; (d) DCP; (e) DehazeNet; (f) FFA-Net; (g) MBE; (h) PSD; (i) SOTSFDE; (j) Ours; (k) Haze-free image Figures 6-8 shows the dehazing results of different algorithms on three indoor images. It can be seen from Figures 6, 7 that compared with other algorithms, the algorithm proposed in this article has better recovery ability for colour information, and is the closest to the original fog-free image. In Figure 8, b, c and e–i have obvious defects. Among them, the overall brightness of Figure 8b–d, f and i is dark, and the colour tone of Figure 8i deviates greatly from the original image. Figure 8g, h has a good restoration ability for edge detail information, but the overall colour is too bright. In the background of Figure 8e, there is still part of the fog that has not been removed, the background targets is relatively vague. In contrast, the image restored by the algorithm proposed in this article is more real and natural. Figures 9-11 show the processing results of different algorithms on three artificially fogged outdoor images. In Figure 9, the overall colour of Figure 9c–g is dark, and the detailed information is relatively fuzzy. In Figure 9b, the entire image presents the overexposed phenomena, the brightness is too high, and the information on the edge of the building is seriously missing. The colour shift in Figure 9i is too large, which seriously affects the visual effect. The effect of Figure 9h and j is relatively good. Though Figure 9h has a better ability to restore detail information, the colour of the building is slightly darker. Figure 9j maintains better building colours and has better colour information recovery capabilities. As Figure 10 shows, Figure 10c, d, g and i have obvious distortions in the sky region, and the restoration ability of Figure 10c–f and i in the ground area is poor. In Figure 10b, the distant sky area is highlighted due to overexposure. The building in Figure 10h has severe black shadow, and the colour of the distant trees is distorted. The image processed by the algorithm proposed in this paper not only avoids the problem of distortion in the sky area, but also has good recovery ability for distant objects. In the comparative experiment in Figure 11, Figure 11j avoids the problem of colour distortion and has a higher degree of restoration capability for colour information than other algorithms. FIGURE 9Open in figure viewerPowerPoint From left to right are the results of (a) Hazy image; (b) BCCR; (c) NLID; (d) DCP; (e) DehazeNet; (f) FFA-Net; (g) MBE; (h) PSD; (i) SOTSFDE; (j) Ours; (k) Haze-free image FIGURE 10Open in figure viewerPowerPoint From left to right are the results of (a) Hazy image; (b) BCCR; (c) NLID; (d) DCP; (e) DehazeNet; (f) FFA-Net; (g) MBE; (h) PSD; (i) SOTSFDE; (j) Ours; (k) Haze-free image FIGURE 11Open in figure viewerPowerPoint From left to right are the results of (a) Hazy image; (b) BCCR; (c) NLID; (d) DCP; (e) DehazeNet; (f) FFA-Net; (g) MBE; (h) PSD; (i)SOTSFDE; (j) Ours; (k) Haze-free image It can be seen from Table 4 that, in the third image, the PSNR of the method of DehazeNet is the best, and the evaluation result of the algorithm we proposed is sub-optimal, with only a difference of 1.1886. The SSIM and PSNR