Image enhancement method and device for monitoring video

Abstract

The invention discloses an image enhancement method and device for a monitoring video, and relates to the technical field of image processing. A target image is decomposed into a hue component, a saturation component and a brightness component; adaptively enhancing the saturation component to obtain a first target component; enhancing the brightness component through a pre-trained image enhancement model to obtain a second target component; and merging and converting the hue component, the first target component and the second target component to obtain an enhanced video frame image. The saturation and brightness information of the image is processed independently, so that the problem of color distortion caused by overall processing is avoided; and the ability of capturing and enhancing image details by the deep learning model is fully utilized, and important texture and edge details are effectively enhanced in a low-brightness environment, so that the enhanced image is clearer and finer, and the enhancement effect of the image is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an image enhancement method and device for monitoring video. Background Art

[0002] Video surveillance plays an important role in security protection, public place monitoring and home security. However, the quality of surveillance videos often drops significantly in low-light conditions, resulting in problems such as insufficient brightness, excessive noise, and lack of details, which seriously affects the normal operation of the surveillance system. In order to ensure the effectiveness and accuracy of surveillance videos, image enhancement technology is particularly necessary in low-light environments.

[0003] Traditional image enhancement methods rely on simple filters or contrast adjustments, which cannot fully capture the detailed features of the image, especially in low-brightness scenes, where important texture and detail information is easily lost. In addition, such methods usually process the image as a whole, and in low-brightness scenes, they fail to fully consider the difference between color information and brightness information, which can easily lead to image color distortion or unnaturalness, resulting in poor image enhancement effects. Summary of the invention

[0004] The purpose of the present invention is to solve the problem of poor enhancement effect mentioned in the above background technology, and to propose an image enhancement method and device for monitoring video.

[0005] A first aspect of the present invention provides an image enhancement method for monitoring video, the method comprising:

[0006] Obtain a target video frame image of a surveillance video and decompose it into a hue component, a saturation component, and a brightness component;

[0007] Adaptively enhancing the saturation component to obtain a first target component;

[0008] Using the brightness component as an input image of a pre-trained image enhancement model to obtain a second target component;

[0009] The hue component, the first target component and the second target component are combined and transformed to obtain an enhanced video frame image.

[0010] Optionally, the adaptively stretching the saturation component to obtain the first target component includes:

[0011] According to the maximum and minimum value normalization method, the saturation component is adaptively stretched to obtain the first target component:

[0012]

[0013] Among them, St (i, j) is the value of the first target component at position (i, j); S0(i, j) is the value of the saturation component at position (i, j); S min is the minimum value of the saturation component, S max is the maximum value of the saturation component.

[0014] Optionally, the image enhancement model includes a multi-scale feature extraction network, a feature enhancement network, a multi-scale feature fusion network and a feature reconstruction output network; the multi-scale feature extraction network includes multiple convolutional layers, and the convolution kernels of different convolutional layers have different sizes; the feature enhancement network includes a convolutional layer for channel expansion and a convolutional layer with symmetric jump connections; wherein:

[0015] The multi-scale feature extraction network is used to receive the input image and output a plurality of feature maps of different sizes:

[0016]

[0017] Wherein, Y1, Y2 and Y3 are feature maps of different sizes; x is the input image; f represents the convolution operation; K1, K2 and K3 are the convolution kernel sizes; S1, S2 and S3 are the convolution steps; σ is the activation function;

[0018] The feature enhancement network is used to perform channel expansion and feature enhancement on different feature maps respectively to obtain multiple feature information maps F1, F2 and F3; F1, F2, F3 correspond to Y1, Y2, Y3 one by one;

[0019] The multi-scale feature fusion network is used to resize different feature information maps, perform feature fusion, and output a fused feature map;

[0020] The feature reconstruction output network is used to reconstruct the fused feature map into a single channel map as a second target component.

[0021] Optionally, the multi-scale feature fusion network includes a downsampling layer, a first summation layer, a channel segmentation layer, an attention mechanism layer, a second summation layer, an upsampling layer and a superposition layer; the attention mechanism layer includes a plurality of attention blocks; wherein:

[0022] The downsampling layer is used to downsample the feature information graphs F1 and F2 respectively to obtain feature graphs G1 and G2; the sizes of G1, G2 and F3 are the same;

[0023] The first summation layer is used to add the feature map G1 and the feature map G2 to obtain a feature map G];

[0024] The channel segmentation layer is used to divide the feature map G3 into multiple feature blocks along the channel dimension;

[0025] The attention mechanism layer is used to process multiple feature blocks using multiple attention blocks to obtain multiple corresponding enhanced feature blocks; and to splice the multiple enhanced feature blocks to obtain a feature map G4;

[0026] The second summation layer is used to add the feature map G4 and the feature map F3 to obtain the feature map F4;

[0027] The upsampling layer is used to upsample the feature map F4 to obtain a feature map F5; F5 has the same size as the input image;

[0028] The superposition layer is used to splice the feature map F5 and the input image along the channel dimension to obtain a fused feature map.

[0029] Optionally, the feature reconstruction output network includes a pooling layer, a fully connected layer, a weighted fusion layer and a single-channel reconstruction layer; wherein:

[0030] The pooling layer is used to average pool each channel of the fused feature map and output a feature vector;

[0031] The fully connected layer is used to receive the feature vector as input and output a weight vector;

[0032] The weighted fusion layer is used to weight each channel of the fused feature map according to the weight vector to obtain a target feature map:

[0033] P t (m, n, c) = P0 (m, n, c) * A (c)

[0034] Among them, P t (m, n, c) is the value of the target feature map at position (m, n, c); P0(m, n, c) is the value of the fused feature map at position (m, n, c); A(c) is the value of the weight vector at position c;

[0035] The single-channel reconstruction layer is used to convert the target feature map into a single-channel map as a second target component.

[0036] A second aspect of the present invention provides an image enhancement device for monitoring video, the device comprising:

[0037] A conversion module is used to obtain a target video frame image of a surveillance video and decompose it into a hue component, a saturation component and a brightness component;

[0038] A saturation adjustment module, used for adaptively enhancing the saturation component to obtain a first target component;

[0039] A brightness enhancement module, used for taking the brightness component as an input image of a pre-trained image enhancement model to obtain a second target component;

[0040] The rotation module is used to merge and transform the hue component, the first target component and the second target component to obtain an enhanced video frame image.

[0041] Optionally, the saturation adjustment module includes:

[0042] The saturation balance calculation module is used to adaptively stretch the saturation component according to the maximum and minimum value normalization method to obtain the first target component:

[0043]

[0044] Among them, S t (i, j) is the value of the first target component at position (i, j); S0(i, j) is the value of the saturation component at position (i, j); S min is the minimum value of the saturation component, S max is the maximum value of the saturation component.

[0045] Optionally, the image enhancement model includes a multi-scale feature extraction network, a feature enhancement network, a multi-scale feature fusion network and a feature reconstruction output network; the multi-scale feature extraction network includes multiple convolutional layers, and the convolution kernels of different convolutional layers have different sizes; the feature enhancement network includes a convolutional layer for channel expansion and a convolutional layer with symmetric jump connections; wherein:

[0046] The multi-scale feature extraction network is used to receive the input image and output a plurality of feature maps of different sizes:

[0047]

[0048] Wherein, Y1, Y2 and Y3 are feature maps of different sizes; x is the input image; f represents the convolution operation; K1, K2 and K3 are the convolution kernel sizes; S1, S2 and S3 are the convolution steps; σ is the activation function;

[0049] The feature enhancement network is used to perform channel expansion and feature enhancement on different feature maps respectively to obtain multiple feature information maps F1, F2 and F3; F1, F2, F3 correspond to Y1, Y2, Y3 one by one;

[0050] The multi-scale feature fusion network is used to resize different feature information maps, perform feature fusion, and output a fused feature map;

[0051] The feature reconstruction output network is used to reconstruct the fused feature map into a single channel map as a second target component.

[0052] Optionally, the multi-scale feature fusion network includes a downsampling layer, a first summation layer, a channel segmentation layer, an attention mechanism layer, a second summation layer, an upsampling layer and a superposition layer; the attention mechanism layer includes a plurality of attention blocks; wherein:

[0053] The downsampling layer is used to downsample the feature information graphs F1 and F2 respectively to obtain feature graphs G1 and G2; the sizes of G1, G2 and F3 are the same;

[0054] The first summation layer is used to add the feature map G1 and the feature map G2 to obtain a feature map G3;

[0055] The channel segmentation layer is used to divide the feature map G3 into multiple feature blocks along the channel dimension;

[0056] The attention mechanism layer is used to process multiple feature blocks using multiple attention blocks to obtain multiple corresponding enhanced feature blocks; and to splice the multiple enhanced feature blocks to obtain a feature map G4;

[0057] The second summation layer is used to add the feature map G4 and the feature map F3 to obtain the feature map F4;

[0058] The upsampling layer is used to upsample the feature map F4 to obtain a feature map F5; F5 has the same size as the input image;

[0059] The superposition layer is used to splice the feature map F5 and the input image along the channel dimension to obtain a fused feature map.

[0060] Optionally, the feature reconstruction output network includes a pooling layer, a fully connected layer, a weighted fusion layer and a single-channel reconstruction layer; wherein:

[0061] The pooling layer is used to average pool each channel of the fused feature map and output a feature vector;

[0062] The fully connected layer is used to receive the feature vector as input and output a weight vector;

[0063] The weighted fusion layer is used to weight each channel of the fused feature map according to the weight vector to obtain a target feature map:

[0064] P t (m, n, c) = P0 (m, n, c) * A (c)

[0065] Among them, P t(m, n, c) is the value of the target feature map at position (m, n, c); P0(m, n, c) is the value of the fused feature map at position (m, n, c); A(c) is the value of the weight vector at position c;

[0066] The single-channel reconstruction layer is used to convert the target feature map into a single-channel map as a second target component.

[0067] Beneficial effects of the present invention:

[0068] The present invention proposes an image enhancement method for monitoring videos, which comprises: obtaining a target video frame image of the monitoring video and decomposing it into a hue component, a saturation component and a brightness component; adaptively enhancing the saturation component to obtain a first target component; using the brightness component as an input image of a pre-trained image enhancement model to obtain a second target component; and merging and converting the hue component, the first target component and the second target component to obtain an enhanced video frame image.

[0069] By processing the saturation and brightness information of the image separately, the color distortion problem caused by the overall processing is avoided; the brightness component is processed using a pre-trained image enhancement model, making full use of the deep learning model's ability to capture and enhance image details. In low-light environments, it can effectively enhance important texture and edge details, making the enhanced image clearer and more delicate, thereby improving the image enhancement effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The present invention will be further described below in conjunction with the accompanying drawings.

[0071] Figure 1 A flowchart of an image enhancement method for monitoring video is provided for an embodiment of the present invention;

[0072] Figure 2 A structural schematic diagram of an image enhancement model is provided for an embodiment of the present invention;

[0073] Figure 3 A structural diagram of an image enhancement device for monitoring video is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] The embodiment of the present invention provides an image enhancement method for monitoring video. Figure 1 , Figure 1 A flowchart of an image enhancement method for monitoring video provided by an embodiment of the present invention. The method comprises the following steps:

[0076] S101, obtaining a target video frame image of a surveillance video, and decomposing the target video frame image into a hue component, a saturation component, and a brightness component.

[0077] S102, adaptively enhancing the saturation component to obtain a first target component.

[0078] S103, using the brightness component as an input image of a pre-trained image enhancement model to obtain a second target component.

[0079] S104, combining and transforming the hue component, the first target component and the second target component to obtain an enhanced video frame image.

[0080] An image enhancement method for surveillance video provided by an embodiment of the present invention avoids the color distortion problem caused by overall processing by separately processing the saturation and brightness information of the image; uses a pre-trained image enhancement model to process the brightness component, fully utilizing the deep learning model's ability to capture and enhance image details, and can effectively enhance important texture and edge details in a low-brightness environment, making the enhanced image clearer and more delicate, thereby improving the image enhancement effect.

[0081] In one embodiment, step S102 includes:

[0082] According to the maximum and minimum value normalization method, the saturation component is adaptively stretched to obtain the first target component:

[0083]

[0084] Among them, S t (i, j) is the value of the first target component at position (i, j); S0(i, j) is the value of the saturation component at position (i, j); S min is the minimum value of the saturation component, S max is the maximum value of the saturation component.

[0085] In one implementation, by dynamically adjusting the saturation range, the colors of low-saturation areas (such as gray or blurred areas) in the image are made more saturated, while the colors of high-saturation areas are kept from being over-enhanced, thereby adaptively improving the image color contrast and details. Maximum and minimum value normalization can prevent the loss of color information due to low or high saturation in local areas (such as extremely dark or extremely bright areas). By stretching the entire saturation range, more areas in the image can get the appropriate color enhancement effect, improving the overall visual quality of the video.

[0086] In one embodiment, see Figure 2 , Figure 2 A schematic diagram of the structure of an image enhancement model provided by an embodiment of the present invention. As shown in the figure, the image enhancement model includes a multi-scale feature extraction network, a feature enhancement network, a multi-scale feature fusion network and a feature reconstruction output network; the multi-scale feature extraction network includes multiple convolutional layers, and the convolution kernels of different convolutional layers have different sizes; the feature enhancement network includes a convolutional layer for channel expansion and a convolutional layer with symmetrical jump connections;

[0087] in:

[0088] A multi-scale feature extraction network is used to receive an input image and output multiple feature maps of different sizes:

[0089]

[0090] Among them, Y1, Y2 and Y3 are feature maps of different sizes; x is the input image; f represents the convolution operation; K1, K2 and K3 are the convolution kernel sizes; S1, S2 and S3 are the convolution steps; σ is the activation function.

[0091] The feature enhancement network is used to perform channel expansion and feature enhancement on different feature maps respectively to obtain multiple feature information maps F1, F2 and F3; F1, F2, F3 correspond to Y1, Y2, Y3 one by one.

[0092] The multi-scale feature fusion network is used to resize different feature information maps, perform feature fusion, and output a fused feature map.

[0093] The feature reconstruction output network is used to reconstruct the fused feature map into a single channel map as the second target component.

[0094] In one implementation, Figure 2 As described above, the multi-scale feature extraction network can be composed of convolutional layers co nv1 (convolution kernel size K = 7 × 7, step size S = 2), co n v 2(K=5×5, S=2) and con v3 (K = 3 × 3, S = 2). ReL is used after each convolutional layer. u As an activation function. By using convolution kernels of different sizes, image information of different levels and scales can be captured. For example, a small-sized convolution kernel can extract local details, while a large-sized convolution kernel can extract global structural features. This can take into account both the retention of details and the understanding of global information.

[0095] In one implementation, the feature enhancement network can be composed of a convolutional layer v 4. conv 5. conv 6. conv 7. conv 8. conv 9. conv 1 0. Their convolution kernel sizes are all 3×3, and the convolution step size is 1. co n v 4 is used for channel expansion, which increases the dimension of the feature map, allowing the network to process more image information, which helps to improve the enhancement effect. In addition, the symmetrical jump connection can connect shallow features with deep features, retain more image detail information, and avoid feature loss caused by deep convolution. This structure ensures that important edge information and detail features are not lost during the enhancement process, improving the overall enhancement effect.

[0096] In one embodiment, the multi-scale feature fusion network includes a downsampling layer, a first summation layer, a channel slicing layer, an attention mechanism layer, a second summation layer, an upsampling layer, and a stacking layer; the attention mechanism layer includes a plurality of attention blocks; wherein:

[0097] The downsampling layer is used to downsample the feature information maps F1 and F2 respectively to obtain feature maps G1 and G2; the sizes of G1, G2 and F3 are the same.

[0098] The first summation layer is used to add the feature map G1 and the feature map G2 to obtain the feature map G3.

[0099] The channel segmentation layer is used to divide the feature map G3 into multiple feature blocks along the channel dimension.

[0100] The attention mechanism layer is used to process multiple feature blocks using multiple attention blocks to obtain multiple corresponding enhanced feature blocks; and concatenate multiple enhanced feature blocks to obtain a feature map G4.

[0101] The second summation layer is used to add the feature map G4 and the feature map F3 to obtain the feature map F4.

[0102] The upsampling layer is used to upsample the feature map F4 to obtain the feature map F5; F5 has the same size as the input image.

[0103] The overlay layer is used to concatenate the feature map F5 and the input image along the channel dimension to obtain a fused feature map.

[0104] In one implementation, the downsampling layer can be composed of a convolutional layer conv11 (K=5×5, S=2), co nv12 (K=3×3, S=2), co nv1 3 (K = 3 × 3, S = 2). Through the downsampling layer and summation structure, the network can effectively integrate feature maps from different levels and scales and enhance the context perception ability of the model.

[0105] In one implementation, the attention mechanism layer uses multiple attention blocks to process feature blocks, which can automatically focus on important feature areas and can use transforme r Attention blocks in the network. This selective attention mechanism enables the network to dynamically identify and enhance details or features that are important to the image, thereby improving the overall enhancement effect, especially in complex or low-contrast images.

[0106] In one implementation, upsampling may use a bilinear interpolation method.

[0107] In one embodiment, the feature reconstruction output network includes a pooling layer, a fully connected layer, a weighted fusion layer, and a single channel reconstruction layer; wherein:

[0108] The pooling layer is used to average pool each channel of the fused feature map and output a feature vector.

[0109] The fully connected layer receives the feature vector as input and outputs a weight vector.

[0110] The weighted fusion layer is used to weight each channel of the fused feature map according to the weight vector to obtain the target feature map:

[0111] P t (m, n, c) = P0 (m, n, c) * A (c)

[0112] Among them, P t (m, n, c) is the value of the target feature map at position (m, n, c); P0(m, n, c) is the value of the fused feature map at position (m, n, c); A(c) is the value of the weight vector at position c.

[0113] The single-channel reconstruction layer is used to convert the target feature map into a single-channel map as the second target component.

[0114] In one implementation, global average pooling is used to reduce the dimension of the feature map and aggregate the spatial information of the feature channels. The weight coefficients of different channels are estimated through the fully connected layer, and the important features are given greater weights. After the fully connected layer, sigmoid is used as the activation function. The weight coefficient is multiplied by the original feature channel to obtain the target feature map, and then a convolutional layer conv14 (K=3×3, S=1) is passed to further improve the network feature expression ability, and then a convolutional layer conv15 (K=1×1, S=1) is processed to obtain a single channel map.

[0115] The embodiment of the present invention provides an image enhancement device for monitoring video. Figure 3 , Figure 3 The structure diagram of an image enhancement device for monitoring video provided by an embodiment of the present invention. The device includes:

[0116] The conversion module is used to obtain the target video frame image of the surveillance video and decompose it into a hue component, a saturation component and a brightness component.

[0117] The saturation adjustment module is used to adaptively enhance the saturation component to obtain a first target component.

[0118] The brightness enhancement module is used to use the brightness component as an input image of a pre-trained image enhancement model to obtain a second target component.

[0119] The rotation module is used to merge and transform the hue component, the first target component and the second target component to obtain an enhanced video frame image.

[0120] An image enhancement device for monitoring video provided based on an embodiment of the present invention avoids the color distortion problem caused by overall processing by separately processing the saturation and brightness information of the image; uses a pre-trained image enhancement model to process the brightness component, fully utilizing the deep learning model's ability to capture and enhance image details, and can effectively enhance important texture and edge details in a low-brightness environment, making the enhanced image clearer and more delicate, thereby improving the image enhancement effect.

[0121] In one embodiment, the saturation adjustment module includes:

[0122] The saturation balance calculation module is used to adaptively stretch the saturation component according to the maximum and minimum value normalization method to obtain the first target component:

[0123]

[0124] Among them, S t(i, j) is the value of the first target component at position (i, j); S0(i, j) is the value of the saturation component at position (i, j); S min is the minimum value of the saturation component, S max is the maximum value of the saturation component.

[0125] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An image enhancement method for monitoring video, characterized in that: The method comprises: Obtain a target video frame image of a surveillance video and decompose it into a hue component, a saturation component, and a brightness component; Adaptively enhancing the saturation component to obtain a first target component; Using the brightness component as an input image of a pre-trained image enhancement model to obtain a second target component; The hue component, the first target component and the second target component are combined and transformed to obtain an enhanced video frame image.

2. The image enhancement method for monitoring video according to claim 1, characterized in that: Adaptively stretching the saturation component to obtain the first target component includes: According to the maximum and minimum value normalization method, the saturation component is adaptively stretched to obtain the first target component: Among them, S t (i, j) is the value of the first target component at position (i, j); S0(i, j) is the value of the saturation component at position (i, j); S min is the minimum value of the saturation component, S max is the maximum value of the saturation component.

3. The image enhancement method for monitoring video according to claim 1, characterized in that: The image enhancement model includes a multi-scale feature extraction network, a feature enhancement network, a multi-scale feature fusion network and a feature reconstruction output network; the multi-scale feature extraction network includes multiple convolutional layers, and the convolution kernels of different convolutional layers have different sizes; The feature enhancement network includes a convolutional layer for channel expansion and a convolutional layer with symmetric jump connections; in: The multi-scale feature extraction network is used to receive the input image and output a plurality of feature maps of different sizes: Wherein, Y1, Y2 and Y3 are feature maps of different sizes; x is the input image; f represents the convolution operation; K1, K2 and K3 are the convolution kernel sizes; S1, S2 and S3 are the convolution steps; σ is the activation function; The feature enhancement network is used to perform channel expansion and feature enhancement on different feature maps respectively to obtain multiple feature information maps F1, F2 and F3; F1, F2, F3 correspond to Y1, Y2, Y3 one by one; The multi-scale feature fusion network is used to resize different feature information maps, perform feature fusion, and output a fused feature map; The feature reconstruction output network is used to reconstruct the fused feature map into a single channel map as a second target component.

4. The image enhancement method for monitoring video according to claim 3, characterized in that: The multi-scale feature fusion network includes a downsampling layer, a first summation layer, a channel slicing layer, an attention mechanism layer, a second summation layer, an upsampling layer and a superposition layer; the attention mechanism layer includes a plurality of attention blocks; wherein: The downsampling layer is used to downsample the feature information graphs F1 and F2 respectively to obtain feature graphs G1 and G2; the sizes of G1, G2 and F3 are the same; The first summation layer is used to add the feature map G1 and the feature map G2 to obtain a feature map G3; The channel segmentation layer is used to divide the feature map G3 into multiple feature blocks along the channel dimension; The attention mechanism layer is used to process multiple feature blocks using multiple attention blocks to obtain multiple corresponding enhanced feature blocks; and to splice the multiple enhanced feature blocks to obtain a feature map G4; The second summation layer is used to add the feature map G4 and the feature map F3 to obtain the feature map F4; The upsampling layer is used to upsample the feature map F4 to obtain a feature map F5; F5 has the same size as the input image; The superposition layer is used to splice the feature map F5 and the input image along the channel dimension to obtain a fused feature map.

5. The image enhancement method for monitoring video according to claim 3, characterized in that: The feature reconstruction output network includes a pooling layer, a fully connected layer, a weighted fusion layer and a single channel reconstruction layer; wherein: The pooling layer is used to average pool each channel of the fused feature map and output a feature vector; The fully connected layer is used to receive the feature vector as input and output a weight vector; The weighted fusion layer is used to weight each channel of the fused feature map according to the weight vector to obtain a target feature map: P t (m,n,c)=P0(m,n,c)*A(c) Among them, P t (m, n, c) is the value of the target feature map at position (m, n, c); P0(m, n, c) is the value of the fused feature map at position (m, n, c); A(c) is the value of the weight vector at position c; The single-channel reconstruction layer is used to convert the target feature map into a single-channel map as a second target component.

6. An image enhancement device for monitoring video, characterized in that: The device comprises: A conversion module is used to obtain a target video frame image of a surveillance video and decompose it into a hue component, a saturation component and a brightness component; A saturation adjustment module, used for adaptively enhancing the saturation component to obtain a first target component; A brightness enhancement module, used for taking the brightness component as an input image of a pre-trained image enhancement model to obtain a second target component; The rotation module is used to merge and transform the hue component, the first target component and the second target component to obtain an enhanced video frame image.

7. The image enhancement device for monitoring video according to claim 6, characterized in that: The saturation adjustment module comprises: The saturation balance calculation module is used to adaptively stretch the saturation component according to the maximum and minimum value normalization method to obtain the first target component: Wherein, S. (i, j) is the value of the first target component at position (i, j); S0 (i, j) is the value of the saturation component at position (i, j); S min is the minimum value of the saturation component, S max is the maximum value of the saturation component.

8. The image enhancement device for monitoring video according to claim 6, characterized in that: The image enhancement model includes a multi-scale feature extraction network, a feature enhancement network, a multi-scale feature fusion network and a feature reconstruction output network; the multi-scale feature extraction network includes multiple convolutional layers, and the convolution kernels of different convolutional layers have different sizes; The feature enhancement network includes a convolutional layer for channel expansion and a convolutional layer with symmetric jump connections; in: The multi-scale feature extraction network is used to receive the input image and output a plurality of feature maps of different sizes: Wherein, Y1, Y2 and Y3 are feature maps of different sizes; x is the input image; f represents the convolution operation; K1, K2 and K3 are the convolution kernel sizes; S1, S2 and S3 are the convolution steps; σ is the activation function; The feature enhancement network is used to perform channel expansion and feature enhancement on different feature maps respectively to obtain multiple feature information maps F1, F2 and F3; F1, F2, F3 correspond to Y1, Y2, Y3 one by one; The multi-scale feature fusion network is used to resize different feature information maps, perform feature fusion, and output a fused feature map; The feature reconstruction output network is used to reconstruct the fused feature map into a single channel map as a second target component.

9. The image enhancement device for monitoring video according to claim 8, characterized in that: The multi-scale feature fusion network includes a downsampling layer, a first summation layer, a channel slicing layer, an attention mechanism layer, a second summation layer, an upsampling layer and a superposition layer; the attention mechanism layer includes a plurality of attention blocks; wherein: The downsampling layer is used to downsample the feature information graphs F1 and F2 respectively to obtain feature graphs G1 and G2; the sizes of G1, G2 and F3 are the same; The first summation layer is used to add the feature map G1 and the feature map G2 to obtain a feature map G3; The channel segmentation layer is used to divide the feature map G3 into multiple feature blocks along the channel dimension; The attention mechanism layer is used to process multiple feature blocks using multiple attention blocks to obtain multiple corresponding enhanced feature blocks; and to splice the multiple enhanced feature blocks to obtain a feature map G4; The second summation layer is used to add the feature map G4 and the feature map F3 to obtain the feature map F4; The upsampling layer is used to upsample the feature map F4 to obtain a feature map F5; F5 has the same size as the input image; The superposition layer is used to splice the feature map F5 and the input image along the channel dimension to obtain a fused feature map.

10. The image enhancement device for monitoring video according to claim 8, characterized in that: The feature reconstruction output network includes a pooling layer, a fully connected layer, a weighted fusion layer and a single channel reconstruction layer; wherein: The pooling layer is used to average pool each channel of the fused feature map and output a feature vector; The fully connected layer is used to receive the feature vector as input and output a weight vector; The weighted fusion layer is used to weight each channel of the fused feature map according to the weight vector to obtain a target feature map: P t (m,n,c)=P0(m,n,c)*A(c) Among them, P t (m, n, c) is the value of the target feature map at position (m, n, c); P0(m, n, c) is the value of the fused feature map at position (m, n, c); A(c) is the value of the weight vector at position c; The single-channel reconstruction layer is used to convert the target feature map into a single-channel map as a second target component.

Images