CN109360179A - Image fusion method, device and readable storage medium - Google Patents
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Abstract
本发明公开了一种图像融合方法、装置及可读存储介质，应用于图像处理技术领域，图像融合方法包括：首先得到配准后的第一图像和第二图像；经过卷积神经网络训练后分类输出第一得分图和第二得分图；对第一得分图和第二得分图的对应像素进行比较，得到二值图；得到第一融合图像；计算第一结构相似度图，以及计算第二结构相似度图；获得第一结构相似度图和第二结构相似度图的差异图；基于差异图、第一图像和第二图像，得到第二融合图像。应用本发明实施例，通过双通道卷积神经网络得到红外与可见光图像的融合图像，卷积神经网络作为深度学习的算法，可以自动选择图像特征，改善特征提取的单一性，避免了现有红外图像与可见光图像融合方法的缺陷。
The invention discloses an image fusion method, a device and a readable storage medium, which are applied to the technical field of image processing. The image fusion method includes: firstly obtaining a registered first image and a second image; The first score map and the second score map are classified and output; the corresponding pixels of the first score map and the second score map are compared to obtain a binary map; the first fusion image is obtained; the first structural similarity map is calculated, and the first Two structural similarity maps; obtaining a difference map between the first structural similarity map and the second structural similarity map; and obtaining a second fusion image based on the difference map, the first image, and the second image. By applying the embodiment of the present invention, a fusion image of infrared and visible light images is obtained through a dual-channel convolutional neural network. As a deep learning algorithm, the convolutional neural network can automatically select image features, improve the singleness of feature extraction, and avoid the existing infrared Flaws of image and visible light image fusion methods.
Description
Technical field
The present invention relates to people's image fusion technology field more particularly to a kind of image interfusion methods, device and readable storage
Medium.
Background technique
Infrared sensor is sensitive to the infrared thermal characteristics of target area, it can with work double tides and overcome illumination difficulty come
It was found that target, but it often lacks detailed information abundant, blurred background；And visible images include more abundant texture
Feature and detailed information, but its image-forming condition is to the more demanding of illumination.If by infrared image letter complementary with visible images
Breath carries out effective integration, and the blending image information of acquisition is richer, robustness is stronger, is subsequent image segmentation, detection, identification
It haves laid a good foundation.Therefore infrared and visual image fusion technology is widely used in military and security monitoring field.
Image co-registration is divided into: Pixel-level, feature level and decision level.The image co-registration of the Pixel-level figure of basis and fusion the most
As information is richer.Image interfusion method based on multi-scale transform (MST) and rarefaction representation (SR) is pixel-level image fusion
Most common method in method, image characteristics extraction device needs manual designs in such method, and operation efficiency is low；It mentions simultaneously
The single characteristics of image got not is the image-context that can be applied to all kinds of complexity well, is easy in the region of uniform gray level
Erroneous judgement.
Summary of the invention
The embodiment of the present invention is designed to provide a kind of image interfusion method, device and readable storage medium storing program for executing, by double
Channel convolutional neural networks obtain infrared and visible images blending images, calculation of the convolutional neural networks as deep learning
Method can automatically select characteristics of image, improve the unicity of feature extraction, avoid existing infrared image and melt with visible images
The defect of conjunction method.Specific technical solution is as follows:
In order to achieve the above objectives, the embodiment of the invention provides a kind of image interfusion methods, comprising:
Infrared image is registrated with visible images, the first image and the second image after being registrated, wherein institute
It is that visible images are the visible images that state the first image, which be parts of images, second image in the infrared image,
In parts of images；
The first image and second image are input in trained convolutional neural networks, by the convolution
Classification the first shot chart of output and the second shot chart after neural metwork training；
The respective pixel of first shot chart and second shot chart is compared, binary map is obtained；
Based on the binary map, the first image and second image, the first blending image is obtained；
The first structure similarity graph of the first image and first blending image is calculated, and calculates the second image
With the second structural similarity figure of first blending image；
Obtain the disparity map of the first structure similarity graph and the second structural similarity figure；
Based on the disparity map, the first image and second image, the second blending image is obtained.
In a kind of implementation, the respective pixel to first shot chart and second shot chart compares
Compared with the step of obtaining binary map, comprising:
For the first pixel on first shot chart, judge whether the pixel value greater than the second pixel, wherein
First pixel is any one pixel on first shot chart, and second pixel is second score
Pixel corresponding with first pixel on figure；
If it is, the pixel value of third pixel is 1 in the binary map；Otherwise, the pixel value of third pixel
It is 0, wherein the third pixel is the pixel in the binary map with the first pixel corresponding position.
In a kind of implementation, first blending image embodies formula are as follows:
F1(x, y)=D1(x,y)A(x,y)+(1-D1(x,y)B(x,y))
Wherein, D1For binary map, A is the first image, and B is the second image, F1For the first blending image, x, y are to constitute pixel
The coordinate value of point.
In a kind of implementation, the difference for obtaining the first structure similarity graph and the second structural similarity figure
The step of different figure, comprising:
Obtain the difference of the first structure similarity graph and the second structural similarity figure；
Using the absolute value of the difference as the difference of the first structure similarity graph and the second structural similarity figure
Different figure.
It is described to be based on the disparity map, the first image and second image in a kind of implementation, obtain second
The step of blending image includes:
Based on target area, region unrelated with target in the disparity map is removed, obtains target's feature-extraction image；
According to the target's feature-extraction image, the first image and second image, the second blending image is obtained.
In a kind of implementation, second blending image embodies formula are as follows:
F2(x, y)=D2(x,y)A(x,y)+(1-D2(x,y)B(x,y))
Wherein, D2For target's feature-extraction image, A is the first image, and B is the second image, and x, y are the seat for constituting pixel
Scale value, F2For the second blending image.
By the binary map as decision diagram, initial fusion image is obtained using Weighted Fusion rule, is finally mentioned using SSIM
The notable figure for taking out target area, merges again, obtains final blending image；
In a kind of implementation, the training step of the convolutional neural networks, comprising:
The first quantity original image having a size of 32 × 32 is extracted from the first image set, and is added in the second image set
Second quantity visible images；
The original image and the visible images are converted into grayscale image, and the above gray level image is cut into 16 ×
16 sub-block, as high resolution graphics image set；
Gaussian Blur processing is carried out to the first quantity original image that the first image is concentrated, and the second image is added
The infrared light image for the second quantity concentrated, then first quantity is opened into original image and the second quantity infrared light image
It is cut into 16 × 16 sub-block, as fuzzy graph image set.
Convolutional neural networks structure is trained on the fuzzy graph image set and high resolution graphics image set made.
In a kind of implementation, the convolutional neural networks are binary channels network, each channel is by 5 layers of convolution
Neural network is constituted, including 3 convolutional layers, and 1 maximum pond layer and 1 full articulamentum, last output layer are 1
Softmax classifier.
In addition, the embodiment of the invention also provides a kind of image fusion devices, comprising:
Registration module, for being registrated to infrared image with visible images, the first image after being registrated and
Two images, wherein the first image is that parts of images, second image in the infrared image are that visible images are
Parts of images in the visible images；
Categorization module, for the first image and second image to be input to trained convolutional neural networks
In, classification the first shot chart of output and the second shot chart after convolutional neural networks training；
Comparison module is compared for the respective pixel to first shot chart and second shot chart, obtains
Binary map；
First Fusion Module obtains first and melts for being based on the binary map, the first image and second image
Close image；
Computing module, for calculating the first structure similarity graph of the first image Yu first blending image, with
And calculate the second structural similarity figure of the second image and first blending image；
Module is obtained, for obtaining the disparity map of the first structure similarity graph and the second structural similarity figure；
Second Fusion Module obtains second and melts for being based on the disparity map, the first image and second image
Close image.
And a kind of readable storage medium storing program for executing is provided, and it is stored thereon with computer program, it is real when which is executed by processor
The step of incumbent item of image fusion method.
Using a kind of image interfusion method provided in an embodiment of the present invention, device and readable storage medium storing program for executing, pass through convolution mind
Infrared and visible images blending images are obtained through network, characteristics of image is automatically selected, improves the unicity of feature extraction, keep away
The defect of existing infrared image and visible light image fusion method is exempted from.For binary segmentation there is no completely by target area with
Background area is accurately divided, and the case where shade occurs so as to cause the blending image in later period, according to infrared and visible light source figure
Conspicuousness target area figure is obtained as the difference with the structural similarity of original fusion image, secondary fusion steps is taken to change
Kind fused image quality, the fusion method based on conspicuousness can keep the integrality of prominent target area, and improve fusion figure
The visual quality of picture, so as to preferably serve subsequent image understanding and identification etc..
Detailed description of the invention
Fig. 1 is the flow diagram of image interfusion method provided in an embodiment of the present invention；
Fig. 2 is the first effect diagram provided in an embodiment of the present invention；
Fig. 3 is second of effect diagram provided in an embodiment of the present invention；
Fig. 4 is the third effect diagram provided in an embodiment of the present invention；
Fig. 5 is the 4th kind of effect diagram provided in an embodiment of the present invention；
Fig. 6 is the 5th kind of effect diagram provided in an embodiment of the present invention；
Fig. 7 is the 6th kind of effect diagram provided in an embodiment of the present invention；
Fig. 8 is the 7th kind of effect diagram provided in an embodiment of the present invention；
Fig. 9 is the 8th kind of effect diagram provided in an embodiment of the present invention；
Figure 10 is the 9th kind of effect diagram provided in an embodiment of the present invention；
Figure 11 is the provided in an embodiment of the present invention ten kind of effect diagram.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
It should be noted that in the image processing arts, the thermoradiation efficiency in infrared image with target is larger, and can
Light-exposed image grayscale differs greatly even opposite；Infrared image background gray scale is low without apparent thermal sensation effect contrast, with
Visible images are compared, and spectral information is lacked, but equally include detailed information.Therefore, only have when being merged to image
More go the information for retaining original image that could further improve syncretizing effect.
Referring to Fig. 1, the embodiment of the invention provides a kind of image interfusion methods, include the following steps:
S101 is registrated infrared image with visible images, the first image and the second image after being registrated,
In, the first image is that parts of images, second image in the infrared image are that visible images are described visible
Parts of images in light image.
It should be noted that geometrical registration refer to different time, different-waveband, different remote sensor systems are obtained same
The image (data) in one area, through geometric transformation make corresponding image points in position with the operation that is overlapped completely in orientation.Specifically
Geometrical registration process is the prior art, and this will not be repeated here for the embodiment of the present invention.
It is understood that sliding window is the commonly used image processing tool in image procossing, specifically, sliding window
The size of mouth can be 3*3,5*5 either 16*16 etc., and the embodiment of the present invention is not specifically limited herein.
Illustratively, by taking the first image as an example, the sliding window of 16*16 can be opened from first pixel in the upper left corner
Begin, as first central pixel point of 16*16 sliding window, then successively moves the 16*16 sliding window.So the
The chance of pixel centered on any one pixel in one image has, then and so on, for the second image
Be in this way, so any one central pixel point in the first image can be calculated according to this principle, and it is right in the second image
Answer the structural similarity of central pixel point.
Sliding window is defined having a size of 16 × 16, step-length 1, the infrared image being registrated and visible images of input are distinguished
It is done from left to right in infrared image and visible images, slide from top to bottom obtains the first image of infrared image sub-block
VA, as shown in Figure 2；The second image of visible images sub-block VB, as shown in Figure 3.
The first image and second image are input in trained convolutional neural networks, by institute by S102
Classification exports the first shot chart and the second shot chart after stating convolutional neural networks training.
It should be noted that convolutional neural networks are a kind of depth feed forward-fuzzy controls in machine learning, have become
It is applied to image recognition to function.Convolutional neural networks are a kind of feedforward neural networks, and artificial neuron can respond surrounding cells,
It can carry out large-scale image procossing, including convolutional layer and pond layer.
In a kind of implementation, the training step of the convolutional neural networks, comprising: from the first image set extract having a size of
32 × 32 the first quantity original image, and the second quantity visible images being added in the second image set；By the original
Beginning image and the visible images are converted into grayscale image, and the above gray level image is cut into 16 × 16 sub-block, as height
Resolution chart image set；Gaussian Blur processing is carried out to the first quantity original image that the first image is concentrated, and is added the
The infrared light image of the second quantity in two image sets, then first quantity original image and second quantity is infrared
Light image is cut into 16 × 16 sub-block, as fuzzy graph image set.
Illustratively, 2000 original clear images having a size of 32 × 32 are extracted from Cifar-10 image set, and added
Enter 200 visible images in TNO_Image_Fusion_Datase image set, is then converted into grayscale image and image is complete
Portion is cut into 16 × 16 sub-block, as high resolution graphics image set；Secondly high to all being carried out from Cifar-10 image subblock
This Fuzzy Processing (since infrared light image background area is low compared with visible images resolution ratio), and TNO_Image_ is added
200 infrared light images (sub-block for having been entirely cut into 16 × 16) in Fusion_Datase image set, as fuzzy graph image set.
Using binary channels network, each channel is made of 5 layers of convolutional neural networks, including 3 convolutional layers, and 1
Maximum pond layer and 1 full articulamentum, last output layer are 1 softmax classifiers.Input picture block size is 16
× 16, the convolution kernel size of convolutional layer is set as 3 × 3, and step-length is set as 1；Maximum pond layer convolution kernel size 2 × 2, step-length 2 swash
Function living is Relu.Momentum and weight decaying are set to 0.9 and 0.0005, learning rate 0.0001.
It is understood that the first image is input in trained convolutional neural networks, by the convolution
Neural network is trained each of the first image pixel, obtains the score to each pixel, thus right
All pixels point in first image obtains the score of all pixels point after being trained, thus the first score after being trained
Scheme SA, similarly, the corresponding second shot chart S of the second image can be obtainedB.Detailed process is shown in Figure 4, in convolutional Neural net
Network exports the image after training after convolution twice, maximum pond, convolution sum connect entirely.
S103 is compared the respective pixel of first shot chart and second shot chart, obtains binary map.
Specifically, judging whether the pixel greater than the second pixel for the first pixel on first shot chart
Value, wherein first pixel is any one pixel on first shot chart, and second pixel is described
Pixel corresponding with first pixel on second shot chart；If it is, the third pixel in the binary map
Pixel value is 1；Otherwise, the pixel value of third pixel be 0, wherein the third pixel be the binary map on it is described
The pixel of first pixel corresponding position.
For binary map T, the first shot chart and the second shot chart are subjected to individual element comparison, if any one pixel
Point, position are (m, n), value SAPixel point value be greater than SBRespective pixel point value, then the pixel is corresponding in binary map
Value is 1 at position (m, n), conversely, then the pixel in the corresponding position of binary map obtains 0, shown in following formula, illustratively,
Based on Fig. 2 and Fig. 3, the binary map obtained after through neural network shown in Fig. 4 is as shown in Figure 5.
The binary map of a target area and background area is thus obtained, wherein white area indicates infrared image
Target area, black region is background area, which can be used as the decision diagram of image co-registration.
S104 is based on the binary map, the first image and second image, obtains the first blending image.
First image and the second image are weighted according to binary map can obtain initial fusion as a result, initial fusion purpose
That the background area of the target area of infrared image and high-resolution visible images is integrated into an image, based on Fig. 2,
Fig. 3 and Fig. 5 obtains the first blending image as shown in FIG. 6.
In a kind of implementation, first blending image embodies formula are as follows:
F1(x, y)=D1(x,y)A(x,y)+(1-D1(x,y)B(x,y))
Wherein, D1For binary map, A is the first image, and B is the second image, F1For the first blending image, x, y are to constitute pixel
The coordinate value of point.
S105 calculates the first structure similarity graph of the first image and first blending image, and calculating the
Second structural similarity figure of two images and first blending image.
There are very strong relevance between infrared image and visible images pixel, there is a large amount of among these relevances
Structural information, image structure similarity SSIM (structural similarity index) are that one kind is used to assess image matter
The index of amount.From the perspective of image construction, structural information is defined as brightness and contrast by structural similarity index, with this
To reflect the structural of objects in images.For two images C and D, then the similarity measure function of two images is defined as:
Wherein, μa, μbIt is the average gray of image C and D, σa, μbIt is the standard deviation of image C and D, σabIt is image C and D
Covariance, C1, C2, C3It is minimum normal number, it is therefore an objective to unstable caused by when avoiding denominator close to 0.α, beta, gamma > 0 are to use
To adjust brightness, contrast, the weight of structure function.
Therefore, the first image A and the first blending image F is calculated1First structure similarity graph SAF, illustratively, it is based on
Fig. 2 and Fig. 6 obtains first structure similarity graph as shown in Figure 7, calculates the second image B and the first blending image F1The second knot
Structure similarity graph SBF, the second structural similarity figure as shown in Figure 8 is obtained based on Fig. 3 and Fig. 6.
S106 obtains the disparity map of the first structure similarity graph and the second structural similarity figure.
In a kind of implementation, the difference for obtaining the first structure similarity graph and the second structural similarity figure
The step of different figure, comprising: obtain the difference of the first structure similarity graph and the second structural similarity figure；By the difference
Disparity map of the absolute value of value as the first structure similarity graph and the second structural similarity figure.Specifically, first
Structural similarity figure and the second structural similarity figure disparity map are as follows:
S=| SAF-SBF|
Wherein, first structure similarity graph SAF, the second structural similarity figure SBF, S is disparity map, illustratively, based on figure
The disparity map that 7 and Fig. 8 is obtained is as shown in Figure 9.
S107 is based on the disparity map, the first image and second image, obtains the second blending image.
Since the first blending image that initial fusion obtains completely does not divide target area accurately with background area,
Cause the blending image in later period shade occur, therefore takes secondary fusion steps to improve fused image quality.
It is described to be based on the disparity map, the first image and second image in a kind of implementation, obtain second
The step of blending image includes: to be removed region unrelated with target in the disparity map based on target area, obtained target signature
Extract image；According to the target's feature-extraction image, the first image and second image, the second fusion figure is obtained
Picture.
Illustratively, it is based on disparity map shown in Fig. 9, obtains target's feature-extraction image as shown in Figure 10.
In a kind of implementation, second blending image embodies formula are as follows:
F2(x, y)=D2(x,y)A(x,y)+(1-D2(x,y)B(x,y))
Wherein, D2For target's feature-extraction image, A is the first image, and B is the second image, and x, y are the seat for constituting pixel
Scale value, F2For the second blending image.
Regard secondary fusion as infrared image and visual image fusion based on conspicuousness Objective extraction.Disparity map S
Contain the salient region of infrared image.Using morphological images processing method, area unrelated with target in disparity map is removed
Domain obtains target's feature-extraction figure, it is to be appreciated that target area is the target person extracted by infrared sensor
Therefore infrared figure enhances the conspicuousness of target area, so as to improve the detailed information retained in blending image, such as scheme
Shown in 11, the second blending image based on Figure 10 and Fig. 2, Fig. 3 acquisition.
Using the thought of binary segmentation, obtain infrared merging figure with visible images by binary channels convolutional neural networks
Picture, algorithm of the convolutional neural networks as deep learning, can automatically select characteristics of image, improve the unicity of feature extraction,
Avoiding the defect of existing infrared image and visible light image fusion method, (majority needs that manual designs extract feature and feature mentions
Take it is single, be easily lost).Secondly, completely do not divide target area accurately with background area for binary segmentation, thus
The blending image in later period is caused the case where shade occur, according to infrared and visible light source image and original fusion image structure
The difference of similitude obtains conspicuousness target area figure, and secondary fusion steps is taken to improve fused image quality, based on aobvious
The fusion method of work property can keep the integrality of prominent target area, and improve the visual quality of blending image, so as to more
Good serves subsequent image understanding and identification etc..
In addition, the embodiment of the invention also provides a kind of image fusion devices, comprising:
Registration module, for being registrated to infrared image with visible images, the first image after being registrated and
Two images, wherein the first image is that parts of images, second image in the infrared image are that visible images are
Parts of images in the visible images；
Categorization module, for the first image and second image to be input to trained convolutional neural networks
In, classification the first shot chart of output and the second shot chart after convolutional neural networks training；
Comparison module is compared for the respective pixel to first shot chart and second shot chart, obtains
Binary map；
First Fusion Module obtains first and melts for being based on the binary map, the first image and second image
Close image；
Computing module, for calculating the first structure similarity graph of the first image Yu first blending image, with
And calculate the second structural similarity figure of the second image and first blending image；
Module is obtained, for obtaining the disparity map of the first structure similarity graph and the second structural similarity figure；
Second Fusion Module obtains second and melts for being based on the disparity map, the first image and second image
Close image.
And a kind of readable storage medium storing program for executing is provided, and it is stored thereon with computer program, it is real when which is executed by processor
The step of incumbent item of image fusion method.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the scope of the present invention.It is all
Any modification, equivalent replacement, improvement and so within the spirit and principles in the present invention, are all contained in protection scope of the present invention
It is interior.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101673396A (en) * | 2009-09-07 | 2010-03-17 | 南京理工大学 | Image fusion method based on dynamic object detection |
| CN103578092A (en) * | 2013-11-11 | 2014-02-12 | 西北大学 | Multi-focus image fusion method |
| CN103700075A (en) * | 2013-12-25 | 2014-04-02 | 浙江师范大学 | Tetrolet transform-based multichannel satellite cloud picture fusing method |
| CN103793896A (en) * | 2014-01-13 | 2014-05-14 | 哈尔滨工程大学 | Method for real-time fusion of infrared image and visible image |
| US8755597B1 (en) * | 2011-02-24 | 2014-06-17 | Exelis, Inc. | Smart fusion of visible and infrared image data |
| CN106530266A (en) * | 2016-11-11 | 2017-03-22 | 华东理工大学 | Infrared and visible light image fusion method based on area sparse representation |
| CN106709477A (en) * | 2017-02-23 | 2017-05-24 | 哈尔滨工业大学深圳研究生院 | Face recognition method and system based on adaptive score fusion and deep learning |
| CN107194904A (en) * | 2017-05-09 | 2017-09-22 | 西北工业大学 | NSCT area image fusion methods based on supplement mechanism and PCNN |
| CN107578432A (en) * | 2017-08-16 | 2018-01-12 | 南京航空航天大学 | Target recognition method based on fusion of visible light and infrared two-band image target features |
-
2018
- 2018-10-18 CN CN201811214128.2A patent/CN109360179B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101673396A (en) * | 2009-09-07 | 2010-03-17 | 南京理工大学 | Image fusion method based on dynamic object detection |
| US8755597B1 (en) * | 2011-02-24 | 2014-06-17 | Exelis, Inc. | Smart fusion of visible and infrared image data |
| CN103578092A (en) * | 2013-11-11 | 2014-02-12 | 西北大学 | Multi-focus image fusion method |
| CN103700075A (en) * | 2013-12-25 | 2014-04-02 | 浙江师范大学 | Tetrolet transform-based multichannel satellite cloud picture fusing method |
| CN103793896A (en) * | 2014-01-13 | 2014-05-14 | 哈尔滨工程大学 | Method for real-time fusion of infrared image and visible image |
| CN106530266A (en) * | 2016-11-11 | 2017-03-22 | 华东理工大学 | Infrared and visible light image fusion method based on area sparse representation |
| CN106709477A (en) * | 2017-02-23 | 2017-05-24 | 哈尔滨工业大学深圳研究生院 | Face recognition method and system based on adaptive score fusion and deep learning |
| CN107194904A (en) * | 2017-05-09 | 2017-09-22 | 西北工业大学 | NSCT area image fusion methods based on supplement mechanism and PCNN |
| CN107578432A (en) * | 2017-08-16 | 2018-01-12 | 南京航空航天大学 | Target recognition method based on fusion of visible light and infrared two-band image target features |
Non-Patent Citations (6)
| Title |
|---|
| DEJAN DRAJIC等: "Adaptive Fusion of Multimodal Surveillance Image Sequences in Visual Sensor Networks", 《IEEE TRANSACTIONS ON CONSUMER ELECTRONICS》 * |
| YU LIU等: "Infrared and visible image fusion with convolutional neural networks", 《INTERNATIONAL JOURNAL OF WAVELETS, MULTIRESOLUTION AND INFORMATION PROCESSING》 * |
| YU LIU等: "Multi-focus image fusion with a deep convolutional neural network", 《INFORMATION FUSION》 * |
| 张蕾等: "采用非采样Contourlet变换与区域分类的红外和可见光图像融合", 《光学精密工程》 * |
| 王建等: "基于Contourlet的图像融合方法", 《微处理机》 * |
| 马丽娟: "基于多尺度分析的图像融合技术研究", 《中国优秀博硕士学位论文全文数据库(硕士) 信息科技辑》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US12111267B2 (en) | 2019-05-15 | 2024-10-08 | Getac Holdings Corporation | System for detecting surface type of object and artificial neural network-based method for detecting surface type of object |
| CN110415200A (en) * | 2019-07-26 | 2019-11-05 | 西南科技大学 | A method of interslice interpolation for CT images of bone cement implants |
| CN110415200B (en) * | 2019-07-26 | 2022-03-08 | 西南科技大学 | A method for inter-slice interpolation of CT images of bone cement implants |
| CN110555820A (en) * | 2019-08-28 | 2019-12-10 | 西北工业大学 | Image fusion method based on convolutional neural network and dynamic guide filtering |
| CN112683787A (en) * | 2019-10-17 | 2021-04-20 | 神讯电脑(昆山)有限公司 | Object surface detection system and detection method based on artificial neural network |
| CN112686274A (en) * | 2020-12-31 | 2021-04-20 | 上海智臻智能网络科技股份有限公司 | Target object detection method and device |
| CN112686274B (en) * | 2020-12-31 | 2023-04-18 | 上海智臻智能网络科技股份有限公司 | Target object detection method and device |
| CN113378009A (en) * | 2021-06-03 | 2021-09-10 | 上海科技大学 | Binary neural network quantitative analysis method based on binary decision diagram |
| CN113378009B (en) * | 2021-06-03 | 2023-12-01 | 上海科技大学 | Binary decision diagram-based binary neural network quantitative analysis method |
| CN114782296A (en) * | 2022-04-08 | 2022-07-22 | 荣耀终端有限公司 | Image fusion method, device and storage medium |
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