CN201927079U - Rapid real-time integration processing system for visible image and infrared image - Google Patents
Rapid real-time integration processing system for visible image and infrared image Download PDFInfo
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- CN201927079U CN201927079U CN2011200566285U CN201120056628U CN201927079U CN 201927079 U CN201927079 U CN 201927079U CN 2011200566285 U CN2011200566285 U CN 2011200566285U CN 201120056628 U CN201120056628 U CN 201120056628U CN 201927079 U CN201927079 U CN 201927079U
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Abstract
The utility model relates to a rapid real-time integration processing system for a visible image and an infrared image, which can conduct the pixel-level real-time integration for an infrared image obtained by an infrared thermal imager and a visible image obtained by a charge coupled device (CCD) camera, can provide an integrated image with redundant information being deleted and integrated complementary information to a user, and can finally realize the consistent description and expression of a target to be observed in a way of high clearness, high resolution and vast information. The rapid real-time integration processing system comprises at least a visible sensor and at least an infrared sensor, wherein the visible sensor and the infrared sensor are connected with a data collecting device, and the data collecting device is connected with a computer.
Description
Technical field
The utility model relates to a kind of scientific experiment system of rapid image fusion treatment, specifically a kind of quick real time fusion process system for visible images and infrared image.
Background technology
When general multi-sensor device object observing, will carry out the switching of each channel image inevitably, and in handoff procedure, because the consistance of optical axis, the problems such as consistance of visual field size cause observing the reduction of validity.Therefore the quick real time fusion process system that designs a cover visual picture and infrared image can improve observation efficiency greatly, and can provide more effectively to decision-making and help.
In recent years, in all trades and professions, particularly based on the computer vision process field of optical sensor, image fusion technology is by extensive concern and research.More and more accelerating algorithm research to image fusion technology based on the industrial circle of Flame Image Process.Image co-registration belongs to the class in the form of data fusion, is that the multi-source information that the multi-source sensor obtains is mated under certain standard, and synthetic automatically have the more brief expression of large information capacity with formation.The fundamental purpose of doing like this is to make things convenient for tasks such as the decision-making in later stage such as pattern-recognition, is beneficial to computing machine and people's quick judgment processing.
Image co-registration is deleted the redundant information of the multi-source image sequence that the multi-source sensor obtains, and complementary information is in addition comprehensive, and the final consistance that forms high-resolution to the destination objects of observation, high-resolution, high information quantity is described and expressed.In other words, image co-registration also can be regarded the information reduction of certain form as.Data after the reduction, the image after just merging has high reliability, high image quality, the feature of high information quantity had both reduced unnecessary duplicate message, had promoted the information capacity of single image again, allowed image information know easy identification more.
At present, the research of image co-registration has been had the history that surpassed for two more than ten years, especially in recent years, along with the development of various sensor, image fusion technology burns the wind.At first vectoring information/image co-registration development is the development and application of military scientific and technological aspect.The informationization of US military is as the high-tech operation pattern on the fusion judgement decision-making that is based upon various information wartime, and these multi-source informations all derive from all kinds of image informations greatly, as visible light information, infrared light information, low-light information, laser intelligence, radar imagery information or the like.Resulting multiple target detection and identification on fusion information basis, investigation monitor that navigational guidance etc. are used will be more accurate.In addition, being applied in over past ten years aspect non-military is also just flourish, high-caliber commercial production, and such as the research aspect of intelligent robot, image co-registration is to set up the requisite prerequisite link of intelligent robot vision; Aspect the civilian Flame Image Process of taking photo by plane, to carry out image co-registration to remote sensing images too.At present, image co-registration is not also set up complete theoretical research system.Therefore blending algorithm all is a kind of trial under various data processing backgrounds.These data processing methods have comprised filtering method, as Wiener filtering, and Kalman filtering; Probabilistic method is very big like method as Bayes; The data clusters method is as the clustering method based on various judgment criteria; The intelligence computation method is as based on approximate data of heuritic approaches such as ant group algorithm, genetic algorithm or the like.
Visible images and infrared image are the most widely used two class images in the present application.They can express the abundant information feature of observed target at two spectral ranges.Therefore, in imaging system, two class sensors tend to be installed side by side, and guarantee the unification of boresight, thus object observing better.But two class images can't be observed simultaneously, can only switch accordingly.This has brought trouble to the observer, and therefore, how visible images and infrared image being carried out quick real time fusion is the difficult problem that multi-sensor imaging system faces.
The utility model content
The purpose of this utility model is exactly for addressing the above problem, a kind of quick real time fusion process system for visible images and infrared image is provided, it can be with infrared image that obtains from thermal infrared imager and the visible images that obtains from ccd video camera, carry out the real time fusion on the pixel level, to delete redundant information, the fused images of integrated complementary information offers the user, and the final consistance that forms high-resolution to the destination object of observation, high-resolution, high information quantity is described and expressed.
For achieving the above object, the utility model adopts following technical scheme:
A kind of quick real time fusion process system for visible images and infrared image, it comprises at least one visible light sensor and at least one infrared sensor, and they are connected with data acquisition equipment, and data acquisition equipment is connected with computing machine.
Described visible light sensor is connected with data acquisition equipment by data bus with infrared sensor.
The utility model in use, at first read in visible light and Infrared video image, read in computing machine through transmission link and data acquisition unit, by process software undertaken of the sequence image method for registering based on the image shift parameter is mated fast then, obtain the fused images for the treatment of of the thick coupling in relevant position.Adopt the image co-registration processing based on the pixel level of bottom then, its basic step comprises the following aspects:
The correction work of step 2, image.Treat that fused images at first needs to carry out based on accurate correction work such as the smart registration of feature.Image-forming principle according to the multi-source sensor adopts corresponding transformation model accurately to adjust.
The consistance sampling of step 3, image.In order to allow the fusion work of pixel level can be effective fast,, adopt the consistance picture element interpolation to form the image of identical scale size for sensor image with different resolution.
The fusion one by one of step 4, pixel level.
The aftertreatment work of step 5, fused image.Because fused image often can be introduced certain noise in the gray scale performance of integral body, so aftertreatment work generally comprises image denoising, image lifting, image cutting etc.
Be described in detail step 3 below to step 5.At first, image is carried out the wavelet decomposition conversion of multilayer respectively, form turriform Wavelet image coefficient.Then, the turriform Wavelet image coefficient that above-mentioned decomposition is obtained merges according to certain blending algorithm, and promptly the wavelet coefficient for different frequency bands merges respectively, obtains having turriform Wavelet image coefficient after the fusion of relevant block sign.At last, the turriform Wavelet image coefficient after the above-mentioned fusion is carried out inverse wavelet transform reconstruct and form fused image.If syncretizing effect is not enough, can carry out corresponding aftertreatment.
In whole fusion flow process, to the influence of final fusion results bigger comprise the choosing of small echo kind, choosing of the turriform wavelet decomposition number of plies and choosing of fusion criterion algorithm.Especially the selection of fusion criterion is to influence the syncretizing effect maximum aspect that works, and is the core and the key of fusion process, and it has directly determined the computing velocity that merges and the quality of fusion results.The utility model does not adopt traditional single wavelet decomposition module, and employing multi-dimensional direction bank of filters (N-dimensional Directional Filter Banks, NDFB) Surfacelet conversion is as two generations wavelet transformation realization non-self-adapting multi-scale geometric analysis, to the signal travel direction decomposition of any dimension.Generally for the blending algorithm correction of low-frequency range image can be to a great extent the information amount of obtaining of decision entire image, therefore a good blending algorithm must be made optimal selection in the low-frequency range blending algorithm, find through experiment, with the gradient-norm is that piece merges the low frequency characteristic that the low frequency blending algorithm that indicates has taken into full account image, is preferable selection.For the image of high band, blending algorithm at first must be considered these directivity factors, and through experiment confirm, energy value makes the fine degree of image be greatly improved as the high frequency blending algorithm that piece merges sign really.
In image co-registration process based on small echo, image is carried out wavelet decomposition, decompose the coefficient that obtains and be divided into low frequency component and high fdrequency component, need obtain the blocking effect region according to low frequency component, just the ghost image region.At first low frequency component is obtained to merge identification image according to fusion rule, this fusion identification image is a bianry image, if pixel value 1 promptly wherein shows the low frequency component that select left source images; If 0, then select the low frequency component of right source images.After having obtained this identification image, need carry out filtering to identification image, with deletion clutter zone, but keep main signal area, the template of selection 3 * 3 is carried out medium filtering.With bigger template identification image is carried out filtering once more then, to obtain background image, we select the template of 16 * 16 sizes.Then filtered identification image and background image are wanted to subtract, obtain to treat the image of filter field, need carry out filtering to eliminate ghost image, guarantee to keep the absolute mass that does not need filter field simultaneously in order to indicate which zone.Filtering should spread along the edge of image direction, and the assurance image border can not degenerated fuzzy.
The beneficial effects of the utility model are: can provide abundant target image directly perceived more for the observer, the target infrared feature can be superimposed upon on the Visible Light Characteristics.Can carry out registration process to visible light video and infrared video, guarantee the unanimity of video image boresight, can guarantee the two height unanimity spatially simultaneously.Can carry out the consistance cutting to visible light video image and Infrared video image, guarantee the unanimity of the two observation visual field size.Can and merge rear video image three to visible light video image, Infrared video image and carry out while numeral output.And can show stack and non-Overlay at the enterprising line operate of fused images.The utlity model has reasonable in designly, easy to operate, characteristics such as the result is accurately objective have good value for applications.
Description of drawings
Fig. 1 is a structural representation of the present utility model.
Fig. 2 is a processing flow chart of the present utility model.
Wherein, 1. visible light sensor, 2. infrared sensor, 3. data bus, 4. data acquisition equipment, 5. computing machine.
Embodiment
Below in conjunction with accompanying drawing and embodiment the utility model is described further.
Among Fig. 1, it comprises at least one visible light sensor 1 and at least one infrared sensor 2, and they are connected with data acquisition equipment 4 by data bus 3, and data acquisition equipment 4 is connected with computing machine 5.
Among Fig. 2, job step of the present utility model is:
At first read in corresponding visible light sequential image and the infrared sequence image that needs fusion by fusion processing system.Simultaneously according to optical axis regulate input parameter as according to image to carrying out coarse alignment, differ with adjustment.Respectively visible images and infrared image are carried out corresponding image denoising pre-service then.Follow pretreated image according to carry out the smart registration of image based on the method for feature angle point.Send into the image co-registration module and carry out Surfacelet conversion, fusion and answer, and carry out the filtering aftertreatment according to the information that obtains in the conversion and eliminate the fusion blocking effect.Export fused image at last for aftertreatment or observation uses such as pattern-recognitions.
Except that the described technical characterictic of instructions, all the other are those skilled in the art's known technology.
Claims (2)
1. the quick real time fusion process system for visible images and infrared image is characterized in that it comprises at least one visible light sensor and at least one infrared sensor, and they are connected with data acquisition equipment, and data acquisition equipment is connected with computing machine.
2. the quick real time fusion process system for visible images and infrared image as claimed in claim 1 is characterized in that described visible light sensor is connected with data acquisition equipment by data bus with infrared sensor.
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102436576A (en) * | 2011-10-21 | 2012-05-02 | 洪涛 | Multi-scale self-adaptive high-efficiency target image identification method based on multi-level structure |
| CN102800097A (en) * | 2012-07-19 | 2012-11-28 | 中国科学院自动化研究所 | Multi-feature multi-level visible light and infrared image high-precision registering method |
| CN102855621A (en) * | 2012-07-18 | 2013-01-02 | 中国科学院自动化研究所 | Infrared and visible remote sensing image registration method based on salient region analysis |
| CN104427245A (en) * | 2013-08-20 | 2015-03-18 | 三星泰科威株式会社 | Image fusion system and method |
| CN104795017A (en) * | 2015-04-24 | 2015-07-22 | 深圳市虚拟现实科技有限公司 | Display control method and head-mounted display equipment |
| CN104796625A (en) * | 2015-04-21 | 2015-07-22 | 努比亚技术有限公司 | Picture synthesizing method and device |
| CN105096285A (en) * | 2014-05-23 | 2015-11-25 | 南京理工大学 | Image fusion and target tracking system based on multi-core DSP |
| CN105260990A (en) * | 2015-09-18 | 2016-01-20 | 新疆医科大学第一附属医院 | Denoising method of noisy infrared spectral signal |
| CN105510787A (en) * | 2016-01-26 | 2016-04-20 | 国网上海市电力公司 | Portable ultrasonic, infrared and ultraviolet detector based on image synthesis technology |
| CN105894009A (en) * | 2014-05-08 | 2016-08-24 | 韩华泰科株式会社 | IMAGE FUSING METHOD and apparatus |
| CN106952245A (en) * | 2017-03-07 | 2017-07-14 | 深圳职业技术学院 | A kind of processing method and system for visible images of taking photo by plane |
| CN106971385A (en) * | 2017-03-30 | 2017-07-21 | 西安微电子技术研究所 | A kind of aircraft Situation Awareness multi-source image real time integrating method and its device |
| CN107782454A (en) * | 2017-10-11 | 2018-03-09 | 广东电网有限责任公司佛山供电局 | A kind of electric power thermal image analysis method of mobile device |
| CN108510455A (en) * | 2018-03-27 | 2018-09-07 | 长春理工大学 | A kind of laser irradiation device image interfusion method and system |
| CN109429018A (en) * | 2017-08-23 | 2019-03-05 | 纬创资通股份有限公司 | Image processing device and method |
| CN110392230A (en) * | 2018-04-19 | 2019-10-29 | 广东电网有限责任公司 | A kind of substation's unattended system |
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2011
- 2011-03-07 CN CN2011200566285U patent/CN201927079U/en not_active Expired - Lifetime
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102436576A (en) * | 2011-10-21 | 2012-05-02 | 洪涛 | Multi-scale self-adaptive high-efficiency target image identification method based on multi-level structure |
| CN102855621A (en) * | 2012-07-18 | 2013-01-02 | 中国科学院自动化研究所 | Infrared and visible remote sensing image registration method based on salient region analysis |
| CN102800097A (en) * | 2012-07-19 | 2012-11-28 | 中国科学院自动化研究所 | Multi-feature multi-level visible light and infrared image high-precision registering method |
| CN102800097B (en) * | 2012-07-19 | 2015-08-19 | 中国科学院自动化研究所 | The visible ray of multi-feature multi-level and infrared image high registration accuracy method |
| CN104427245A (en) * | 2013-08-20 | 2015-03-18 | 三星泰科威株式会社 | Image fusion system and method |
| CN104427245B (en) * | 2013-08-20 | 2019-04-19 | 韩华泰科株式会社 | Image fusion system and method |
| CN105894009A (en) * | 2014-05-08 | 2016-08-24 | 韩华泰科株式会社 | IMAGE FUSING METHOD and apparatus |
| CN105894009B (en) * | 2014-05-08 | 2020-05-19 | 韩华泰科株式会社 | Image fusion method and image fusion device |
| CN105096285A (en) * | 2014-05-23 | 2015-11-25 | 南京理工大学 | Image fusion and target tracking system based on multi-core DSP |
| CN104796625A (en) * | 2015-04-21 | 2015-07-22 | 努比亚技术有限公司 | Picture synthesizing method and device |
| CN104795017A (en) * | 2015-04-24 | 2015-07-22 | 深圳市虚拟现实科技有限公司 | Display control method and head-mounted display equipment |
| CN105260990B (en) * | 2015-09-18 | 2018-10-09 | 新疆医科大学第一附属医院 | Contaminate the denoising method for infrared spectroscopy signals of making an uproar |
| CN105260990A (en) * | 2015-09-18 | 2016-01-20 | 新疆医科大学第一附属医院 | Denoising method of noisy infrared spectral signal |
| CN105510787A (en) * | 2016-01-26 | 2016-04-20 | 国网上海市电力公司 | Portable ultrasonic, infrared and ultraviolet detector based on image synthesis technology |
| CN106952245A (en) * | 2017-03-07 | 2017-07-14 | 深圳职业技术学院 | A kind of processing method and system for visible images of taking photo by plane |
| CN106952245B (en) * | 2017-03-07 | 2018-04-10 | 深圳职业技术学院 | A kind of processing method and system for visible images of taking photo by plane |
| CN106971385B (en) * | 2017-03-30 | 2019-10-01 | 西安微电子技术研究所 | A kind of aircraft Situation Awareness multi-source image real time integrating method and its device |
| CN106971385A (en) * | 2017-03-30 | 2017-07-21 | 西安微电子技术研究所 | A kind of aircraft Situation Awareness multi-source image real time integrating method and its device |
| CN109429018A (en) * | 2017-08-23 | 2019-03-05 | 纬创资通股份有限公司 | Image processing device and method |
| CN107782454A (en) * | 2017-10-11 | 2018-03-09 | 广东电网有限责任公司佛山供电局 | A kind of electric power thermal image analysis method of mobile device |
| CN108510455A (en) * | 2018-03-27 | 2018-09-07 | 长春理工大学 | A kind of laser irradiation device image interfusion method and system |
| CN108510455B (en) * | 2018-03-27 | 2020-07-17 | 长春理工大学 | Laser irradiator image fusion method and system |
| CN110392230A (en) * | 2018-04-19 | 2019-10-29 | 广东电网有限责任公司 | A kind of substation's unattended system |
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