CN107481205A - A kind of Terahertz image fringes noise processing method and system - Google Patents
A kind of Terahertz image fringes noise processing method and system Download PDFInfo
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Abstract
The present invention relates to terahertz detection array or Infrared Detection Array imaging field, discloses a kind of Terahertz image fringes noise processing method and system, including following content：Fourier transformation is carried out to the Terahertz view data of acquisition, obtains the frequency domain figure for the frequency domain character for characterizing the Terahertz view data；First time bandreject filtering is carried out to the frequency domain figure, for eliminating the periodic noise of the frequency range where fringes noise；To carrying out high-pass filtering by the frequency domain figure of first time bandreject filtering, for decaying or suppressing low frequency component, prominent remaining fringes noise；Second of bandreject filtering is carried out to the frequency domain figure Jing Guo high-pass filtering, second of bandreject filtering is carried out for not filtering out complete fringes noise to first time bandreject filtering；Inverse Fourier transform will be used by the frequency domain figure of second of bandreject filtering, be converted to time-domain diagram, and then effectively Terahertz fringes noise can be eliminated.
Description
Technical field
The present invention relates to terahertz detection array or Infrared Detection Array imaging field, more particularly to a kind of Terahertz image
Fringes noise processing method and system.
Background technology
Terahertz emission refers to electromagnetic wave of the frequency between the 10THz of 0.1THz mono-, and its wave band is in microwave and infrared
Between, belong to Far infrared electric magnetic radiation category.The tera-hertz spectra of material includes abundant physics and chemical information.Meanwhile
The characteristics of due to terahertz emission itself, determine that it can turn into fourier-transform infrared photolatent technology and X at many aspects
The complementary technology of ray technology, terahertz emission have extensively in many basic research fields, commercial Application and Military Application field
Wealthy development space.It also has very big application potential in biology, medical science, microelectronics, agricultural and safety inspection field.Separately
Outside, compared with low-frequency electromagnetic wave, Terahertz frequency is higher, can be used as communication carrier, can be carried within the unit interval more
Information.Terahertz emission directionality is fine, and secure communication is oriented available for the short distance in battlefield.May be used also using terahertz imaging
Obtain higher spatial resolution and the longer depth of field etc..
But the characteristics of just because of terahertz emission itself, wavelength is longer, therefore is carrying out figure using terahertz detector
Often there is interference fringe noise when gathering in shape, and researcher has done numerous studies to this all the time, but is still direct mostly
High frequency filter, interaction noise can not be targetedly filtered out, therefore still there are many deficiencies.And due to collection when striped density
Degree and the uncertainty in direction, so removing interference fringe still has very big difficulty.
Therefore, effectively the fringes noise of Terahertz image effectively can not be eliminated in the prior art.
The content of the invention
The present invention effectively can not be eliminated effectively to solve to exist in the prior art to Terahertz image fringes noise
Technical problem, and then provide a kind of Terahertz image fringes noise processing method and system.
In order to solve the above technical problems, one aspect of the present invention is：A kind of Terahertz image fringes noise
Processing method, including following content：
Fourier transformation is carried out to the Terahertz view data of acquisition, obtains the frequency domain for characterizing the Terahertz view data
The frequency domain figure of feature；
First time bandreject filtering is carried out to the frequency domain figure, the cycle for eliminating the frequency range where fringes noise makes an uproar
Sound；
It is prominent for decaying or suppressing low frequency component to carrying out high-pass filtering by the frequency domain figure of first time bandreject filtering
Remaining fringes noise；
Second of bandreject filtering is carried out to the frequency domain figure Jing Guo high-pass filtering, for not filtered out to first time bandreject filtering
Full fringes noise carries out second of bandreject filtering；
Inverse Fourier transform will be used by the frequency domain figure of second of bandreject filtering, be converted to time-domain diagram.
On the other hand, a kind of Terahertz image fringes noise processing system is additionally provided, including：
Fourier transformation module, for carrying out Fourier transformation to the Terahertz view data, obtain described in characterizing too
The frequency domain figure of the frequency domain character of hertz view data；
First bandstop filter, for the frequency domain figure carry out first time bandreject filtering, eliminate fringes noise where
The periodic noise of frequency range；
Frequency domain high-pass filter, for carrying out high-pass filtering to the frequency domain figure for passing through first time bandreject filtering, for decaying
Or suppress low frequency component, prominent remaining fringes noise；
Second bandstop filter, for carrying out second of bandreject filtering to the frequency domain figure Jing Guo high-pass filtering, to for the first time
Bandreject filtering does not filter out second of bandreject filtering of carry out of complete fringes noise；
Inverse Fourier transform module, for inverse Fourier transform will to be used by the frequency domain figure of second of bandreject filtering, turn
It is changed to time-domain diagram.
The beneficial effects of the invention are as follows：It is different from the situation of prior art：
Due to carrying out Fourier to the Terahertz view data of acquisition in the Terahertz image fringes noise processing method
Conversion, a part of fringes noise is then removed by first time bandreject filtering, then, by high-pass filtering, second of band resistance filter
Ripple filters out remaining fringes noise, finally by inverse Fourier transform, is converted to time-domain diagram, obtains the final figure for filtering out noise
Picture, the image protrude object, and wave filter can be set by frequency domain figure, observes simply and easily, it is general to solve algorithm
The problem of property, finally further through image enhaucament so that object is more obvious to be displayed so that the image after processing is not only
Fringes noise is eliminated, also acts the effect of image enhaucament.
Brief description of the drawings
Fig. 1 is the step schematic flow sheet of Terahertz image fringes noise processing method in the embodiment of the present invention；
Fig. 2 a- Fig. 2 f are the design sketch of Terahertz image fringes noise processing procedure in the embodiment of the present invention；
Fig. 3 is the module diagram of Terahertz image fringes noise processing system in the embodiment of the present invention.
Embodiment
The present invention effectively can not effectively be disappeared to solve to exist in the prior art to the fringes noise of Terahertz image
The technical problem removed, and then a kind of Terahertz image fringes noise processing method and system are provided, and then can be effectively to too
Hertz fringes noise is eliminated.
In order to be better understood from technical scheme, below in conjunction with Figure of description and specific embodiment
Technical solution of the present invention is described in detail.
A kind of Terahertz image fringes noise processing method provided in an embodiment of the present invention, as shown in figure 1, including：S101,
Fourier transformation is carried out to the Terahertz view data of acquisition, obtains the frequency for the frequency domain character for characterizing the Terahertz view data
Domain figure；S102, first time bandreject filtering is carried out to the frequency domain figure, the cycle for eliminating the frequency range where fringes noise makes an uproar
Sound；S103, it is prominent for decaying or suppressing low frequency component to carrying out high-pass filtering by the frequency domain figure of first time bandreject filtering
Remaining fringes noise；S104, second of bandreject filtering is carried out to the frequency domain figure Jing Guo high-pass filtering, for hindering band for the first time
Filtering does not filter out complete fringes noise and carries out second of bandreject filtering；S105, by by the frequency domain figure of second of bandreject filtering
Using inverse Fourier transform, time-domain diagram is converted to.
In a particular embodiment, the Terahertz view data of acquisition can be that terahertz imaging system has obtained simultaneously
The view data being stored in memory, from memory read or Terahertz outside imaging system currently implement to gather
The view data of acquisition, specific original image is as shown in Figure 2 a.
Specifically, Fourier transformation is carried out to the Terahertz view data of the acquisition, obtains and characterize Terahertz view data
Frequency domain character frequency domain figure, specifically include：The frequency spectrum shift frequency of the Terahertz view data obtained using Fourier transform pairs is arrived
Origin so that the frequency distribution of Terahertz image is symmetrical using origin as the center of circle；Then, from this by Fourier transformation
Frequency distribution is obtained on Terahertz image, in addition to the bright spot of the center of circle, symmetrical bright spot set also be present, the bright spot collection is combined into dry
Disturb caused by noise, and the interference signal of periodic regularity.
It is above-mentioned that frequency spectrum shift frequency to the center of circle is being clear that picture frequency is distributed, and can isolate periodically
The interference signal of rule, on the spectrogram of frequency displacement to origin it can be seen that also in the presence of centered on certain point except the center of circle in addition to,
Symmetrical bright spot set, this bright spot set be exactly caused by interfering noise, at this moment can be very intuitively by the position
Placement location bandstop filter eliminates interference.The frequency domain figure specifically obtained after Fourier transformation is as shown in Figure 2 b.
The frequency domain where interference fringe noise is can see due to the frequency domain figure obtained in S101, therefore,
Suitable first bandstop filter can be constructed in S102, by formula and Terahertz corresponding to the first bandstop filter of the construction
Corresponding formula is multiplied after view data Fourier transformation, filters out most fringes noise composition in frequency domain.
First bandstop filter is the frequency for suppressing a circle ring area apart from frequency domain center certain distance, can
For eliminating the periodic noise of certain frequency scope, the selection of the first bandstop filter to carrying out the first bandreject filtering, to have
Body step is as follows：
It is from the formula of the bandstop filter：
Wherein, D0To need the distance of the Frequency point and center frequency prevented, W is the bandwidth of bandstop filter, for big
The small image for M*N, Frequency point (u, v) and the distance at frequency domain center are D0(u, v), its expression formula areH0(u, v) is required bandstop filter formula, when its value is 1
When, to the wave band under this frequency domain completely by when its value is 0, being filtered out completely to the wave band under this frequency domain.According to the frequency domain model
Enclose, obtain the Frequency point and frequency domain centre distance D for needing to prevent0With the broadband W of bandstop filter.So as to obtain the filter of the first band logical
The parameter of ripple device.
Then, in S103, to carrying out high-pass filtering by the frequency domain figure of first time bandreject filtering, for decaying or suppressing
Low frequency component, prominent remaining fringes noise.Specifically, Butterworth is used by the frequency domain figure of first time bandreject filtering to this
Wave filter carries out second order high-pass filtering processing, for decaying or suppressing low frequency component, protrudes remaining fringes noise, specifically,
The Butterworth filter is a kind of filter type in Fourier's frequency domain, and the transmission function of the Butterworth filter is cut
The gradient of disconnected part can be controlled by index n, and the truncation part of the Butterworth filter of low order will not be very steep, and ring effect can
To mitigate or avoid.The characteristics of Butterworth filter is that the frequency response curve in same frequency band is flat to greatest extent, is not had
There is fluctuating, and it is zero to be gradually reduced in suppressed frequency band.On the Bode diagram of the logarithm diagonal frequencies of amplitude, from a certain border angular frequency
Start, amplitude gradually decreases with the increase of angular frequency, tends to minus infinity, and the attenuation rate of second order Butterworth filter is
Per 12 decibels of frequency multiplication.
Specifically, the transmission function of butterworth high pass filter is
Wherein, D1For the cut-off frequency of Butterworth filter, n is the exponent number of Butterworth filter, for controlling bar
The steep of special Butterworth wave filter, for the image that size is M*N, Frequency point (u, v) and the distance at frequency domain center are D1(u,
V), its expression formula is
Frequency domain figure after above-mentioned Butterworth filter is as shown in Fig. 2 c, it can be seen that some of them interference bar
Line is not filtered out totally, and the frequency domain where these interference fringes can be found out by frequency domain figure now, is easy to filter again
Ripple.
Therefore, in S104, second of bandreject filtering is carried out to the frequency domain figure Jing Guo high-pass filtering, for first time band
Resistance filtering does not filter out complete fringes noise and carries out second of bandreject filtering, obtains as shown in Figure 2 d, now should second of band resistance
The second bandstop filter that filtering uses is also required to construct, because during first time bandreject filtering, interference fringe arrangement is tight
Close, photoelectricity position is nearer from center origin on frequency domain figure, is relatively large in diameter, therefore, selected by first bandstop filter
Need the distance value of the Frequency point and center frequency prevented small, bandwidth W values are big；During second of bandreject filtering, interference fringe
Arrange loose, from center origin farther out, diameter is smaller for photoelectricity position on frequency domain figure, so selected by second bandstop filter
The Frequency point that prevents of needs and center frequency distance value it is larger, bandwidth W values are smaller, are exactly the second bandreject filtering specifically
The Frequency point and the distance of center frequency that needs selected by device prevent are more than the frequency that the needs selected by the first bandstop filter prevent
The distance of rate point and center frequency, the bandwidth of the second bandstop filter are less than the bandwidth of the first bandstop filter.
Finally, S105 is performed, inverse Fourier transform will be used by the frequency domain figure of second of bandreject filtering, be converted to time domain
Figure.Specifically as shown in Figure 2 e.
The contrast that can be seen that image by Fig. 2 e, in order to emphasize the entirety of image or local characteristicses, is incited somebody to action original than relatively low
Unsharp image is apparent from or emphasized some features interested, the difference in expanded view picture between different objects feature
Not, suppress uninterested feature, be allowed to improve picture quality, abundant information amount, strengthen image interpretation and recognition effect, meet
The demand of some special analysis, therefore, after by inverse Fourier transform, tonal range is adjusted to the time-domain diagram, obtained such as
Figure shown in Fig. 2 f.
Specifically, linear gradation stretching is exactly done in 0-255 tonal range to time-domain diagram.Specifically, in image
Pixel when being operated, be described as follows with formula：
G (x, y)=f (x, y) * h (x, y), wherein being that f (x, y) is original image；H (x, y) is space transfer function；g(x,
Y) image after being handled is represented.So as to obtain final result figure.
So that the image after above-mentioned processing not only eliminates fringes noise, and also act as the effect of image enhaucament.
Based on identical inventive concept, the embodiment of the present invention additionally provides a kind of Terahertz image fringes noise processing system
System, as shown in figure 3, including：Fourier transformation module 301, the first bandstop filter 302, frequency domain high-pass filter 303, second
Bandstop filter 304, inverse Fourier transform module 305, wherein, fourier transformation module 301, for the Terahertz figure to acquisition
As data carry out Fourier transformation, the frequency domain figure of the frequency domain character of acquisition sign Terahertz view data；First bandstop filter
302, for carrying out first time bandreject filtering, the periodic noise of the frequency range where elimination fringes noise, frequency domain to frequency domain figure
High-pass filter 303, for carrying out high-pass filtering to the frequency domain figure for passing through first time bandreject filtering, for decaying or suppressing low frequency
Component, prominent remaining fringes noise, the second bandstop filter 304, for carrying out second to the frequency domain figure Jing Guo high-pass filtering
Secondary bandreject filtering, does not filter out second of bandreject filtering of carry out of complete fringes noise to first time bandreject filtering, and Fourier is inverse
Conversion module 305, for inverse Fourier transform will to be used by the frequency domain figure of second of bandreject filtering, be converted to time-domain diagram.
In this specific embodiment, the Terahertz image fringes noise processing system also includes image enhancement module 306,
Time-domain diagram for being obtained to the inverse Fourier transform module adjusts tonal range, so as to obtain the figure of image enhaucament.
The embodiment of the present invention is the foregoing is only, is not intended to limit the scope of the invention, it is every to utilize the present invention
The equivalent structure or equivalent flow conversion that specification and accompanying drawing content are made, or directly or indirectly it is used in other related technologies
Field, it is included within the scope of the present invention.
Claims (9)
1. a kind of Terahertz image fringes noise processing method, it is characterised in that including following content：
Fourier transformation is carried out to the Terahertz view data of acquisition, obtains the frequency domain character for characterizing the Terahertz view data
Frequency domain figure；
First time bandreject filtering is carried out to the frequency domain figure, for eliminating the periodic noise of the frequency range where fringes noise；
It is prominent remaining for decaying or suppressing low frequency component to carrying out high-pass filtering by the frequency domain figure of first time bandreject filtering
Fringes noise；
Second of bandreject filtering is carried out to the frequency domain figure Jing Guo high-pass filtering, for not filtered out completely to first time bandreject filtering
Fringes noise carries out second of bandreject filtering；
Inverse Fourier transform will be used by the frequency domain figure of second of bandreject filtering, be converted to time-domain diagram.
2. Terahertz image fringes noise processing method according to claim 1, it is characterised in that passing through by described
The frequency domain figure of secondary bandreject filtering uses inverse Fourier transform, after being converted to time-domain diagram, in addition to：
Tonal range is adjusted to the time-domain diagram.
3. Terahertz image fringes noise processing method according to claim 1, it is characterised in that described pair obtains too
Hertz view data carries out Fourier transformation, obtains the frequency domain figure for the frequency domain character for characterizing the Terahertz view data, specifically
Including：
The frequency spectrum shift frequency of the Terahertz view data obtained using Fourier transform pairs is to origin so that the Terahertz image
Frequency distribution is symmetrical using origin as the center of circle；
Frequency distribution is obtained from the Terahertz image Jing Guo Fourier transformation, in addition to the center of circle bright spot, also there is symmetrical point
The bright spot set of cloth, the bright spot collection is combined into caused by interfering noise, and the interference signal of periodic regularity.
4. Terahertz image fringes noise processing method according to claim 1, it is characterised in that enter to the frequency domain figure
Row first time bandreject filtering, for eliminating the periodic noise of the frequency range where fringes noise, specifically include：
Pair so frequency domain figure is found out on the centrosymmetric bright spot of frequency domain, it is determined that what is needed to use is corresponding with first time bandreject filtering
The first bandstop filter；
First bandstop filter is used for the frequency for suppressing a circle ring area apart from frequency domain center pre-determined distance, wherein institute
The formula for stating bandstop filter is：
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Wherein, D0To need the distance of the Frequency point and center frequency prevented, W is the bandwidth of bandstop filter, is M* for size
N image, Frequency point (u, v) and the distance at frequency domain center are D0(u, v), its expression formula are
Formula corresponding to first bandstop filter is multiplied with corresponding formula after Terahertz view data Fourier transformation,
Filter out the periodic noise in the frequency domain where fringes noise.
5. Terahertz image fringes noise processing method according to claim 1, it is characterised in that to passing through first time band
The frequency domain figure of resistance filtering carries out high-pass filtering, for decaying or suppressing low frequency component, prominent remaining fringes noise, is specially：
Second order high-pass filtering processing is carried out using Butterworth filter to the frequency domain figure by first time bandreject filtering, for declining
Subtract or suppress low frequency component, prominent remaining fringes noise, the transmission function formula of the Butterworth filter is specially：
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6. Terahertz image fringes noise processing method according to claim 4, it is characterised in that the second bandstop filter
The Frequency point that prevents of needs and the distance of center frequency be more than Frequency point and the frequency that the needs of the first bandstop filter prevent
The distance at center, the bandwidth of second bandstop filter are less than the bandwidth of the first bandstop filter, second bandreject filtering
Device is that second of bandreject filtering uses, and the first bandstop filter is what first time bandreject filtering used.
7. Terahertz image fringes noise processing method according to claim 2, it is characterised in that adjusted to the time-domain diagram
Whole tonal range, it is specially：
Linear gradation stretching is done in 0-255 tonal range to the time-domain diagram.
A kind of 8. Terahertz image fringes noise processing system, it is characterised in that including：
Fourier transformation module, for carrying out Fourier transformation to the Terahertz view data of acquisition, obtain and characterize the terahertz
The hereby frequency domain figure of the frequency domain character of view data；
First bandstop filter, for carrying out first time bandreject filtering to the frequency domain figure, eliminate the frequency where fringes noise
The periodic noise of scope；
Frequency domain high-pass filter, for carrying out high-pass filtering to the frequency domain figure for passing through first time bandreject filtering, for decaying or pressing down
Low frequency component processed, prominent remaining fringes noise；
Second bandstop filter, for carrying out second of bandreject filtering to the frequency domain figure Jing Guo high-pass filtering, band for the first time is hindered
Filtering does not filter out second of bandreject filtering of carry out of complete fringes noise；
Inverse Fourier transform module, for inverse Fourier transform will to be used by the frequency domain figure of second of bandreject filtering, be converted to
Time-domain diagram.
9. Terahertz image fringes noise processing system according to claim 8, it is characterised in that also include：
Image enhancement module, for adjusting tonal range to the time-domain diagram.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108537735A (en) * | 2018-04-16 | 2018-09-14 | 电子科技大学 | A kind of image split-joint method of focal plane terahertz imaging |
| CN109146812A (en) * | 2018-08-16 | 2019-01-04 | 上海波汇科技股份有限公司 | A method of the endoscopic images based on frequency domain filtering remove hexagon noise |
| CN109993174A (en) * | 2018-12-25 | 2019-07-09 | 华中科技大学 | A kind of aerial target detection method and system based on noise suppressed |
| CN110245384A (en) * | 2019-05-16 | 2019-09-17 | 中国工程物理研究院激光聚变研究中心 | A kind of parasitic striped removing method and device based on characteristic frequency spectrum bandreject filtering |
| CN110837130A (en) * | 2019-11-22 | 2020-02-25 | 中国电子科技集团公司第四十一研究所 | Target automatic detection algorithm based on millimeter wave/terahertz wave radiation |
| CN110838093A (en) * | 2019-11-05 | 2020-02-25 | 安徽大学 | Efficient and high-quality fMOST or MOST microscopic image stripe noise removing method |
| CN110866874A (en) * | 2019-10-21 | 2020-03-06 | 南京大学 | Method for removing periodic noise in light field reconstruction image based on frequency domain |
| CN111539967A (en) * | 2020-04-24 | 2020-08-14 | 电子科技大学 | Method and system for identifying and processing interference fringe region in terahertz imaging of focal plane |
| CN111784617A (en) * | 2020-06-09 | 2020-10-16 | 国家卫星气象中心(国家空间天气监测预警中心) | Image processing method and device |
| CN111855672A (en) * | 2020-07-29 | 2020-10-30 | 佛山市南海区广工大数控装备协同创新研究院 | Method for detecting COF flexible board defects |
| CN115272137A (en) * | 2022-09-28 | 2022-11-01 | 北京万龙精益科技有限公司 | Real-time fixed mode noise removing method, device, medium and system based on FPGA |
| CN116074645A (en) * | 2022-11-29 | 2023-05-05 | 哈尔滨工业大学 | Active suppression method for image stripe noise |
| CN117830141A (en) * | 2024-03-04 | 2024-04-05 | 奥谱天成(成都)信息科技有限公司 | Method, medium, equipment and device for removing vertical stripe noise of infrared image |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140231648A1 (en) * | 2013-02-20 | 2014-08-21 | Battelle Energy Alliance, Llc | Terahertz imaging devices and systems, and related methods, for detection of materials |
| CN104517270A (en) * | 2014-12-25 | 2015-04-15 | 深圳市一体太赫兹科技有限公司 | Terahertz image processing method and system |
| CN104517269A (en) * | 2014-12-25 | 2015-04-15 | 深圳市一体太赫兹科技有限公司 | Terahertz image strip processing method and system |
| CN104574300A (en) * | 2014-12-25 | 2015-04-29 | 深圳市一体太赫兹科技有限公司 | Terahertz image noise processing method and system |
| US20160216202A1 (en) * | 2013-09-17 | 2016-07-28 | Commissariat à I'énergie atomique et aux énergies alternatives | Terahertz image sensor |
| CN106204490A (en) * | 2016-07-12 | 2016-12-07 | 厦门大学 | A kind of terahertz pulse image de-noising method |
-
2017
- 2017-08-23 CN CN201710732679.7A patent/CN107481205B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140231648A1 (en) * | 2013-02-20 | 2014-08-21 | Battelle Energy Alliance, Llc | Terahertz imaging devices and systems, and related methods, for detection of materials |
| US20160216202A1 (en) * | 2013-09-17 | 2016-07-28 | Commissariat à I'énergie atomique et aux énergies alternatives | Terahertz image sensor |
| CN104517270A (en) * | 2014-12-25 | 2015-04-15 | 深圳市一体太赫兹科技有限公司 | Terahertz image processing method and system |
| CN104517269A (en) * | 2014-12-25 | 2015-04-15 | 深圳市一体太赫兹科技有限公司 | Terahertz image strip processing method and system |
| CN104574300A (en) * | 2014-12-25 | 2015-04-29 | 深圳市一体太赫兹科技有限公司 | Terahertz image noise processing method and system |
| CN106204490A (en) * | 2016-07-12 | 2016-12-07 | 厦门大学 | A kind of terahertz pulse image de-noising method |
Non-Patent Citations (2)
| Title |
|---|
| 邹园园 等: "基于频域滤波的THz图像条纹噪声处理", 《S计算机工程与应用》 * |
| 陈霖: "连续太赫兹波图像去噪与对比度增强研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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