CN109389563A - It is a kind of based on the random noise self-adapting detecting of sCMOS camera and bearing calibration - Google Patents
It is a kind of based on the random noise self-adapting detecting of sCMOS camera and bearing calibration Download PDFInfo
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Abstract
It is a kind of based on the random noise self-adapting detecting of sCMOS camera and bearing calibration, belong to imaging sensor and field of signal processing.Solves the problems, such as the destruction of random noise effective information caused by the image of sCMOS camera.High frame per second characteristic of the present invention according to sCMOS camera, by the way that under same static state, several continuous shooting images ask cross-referenced method, the detection of Lai Shixian random noise.By detecting acquired results, the method combined using Principle of Statistics with simple median filter is only corrected the noise spot detected.The present invention can quickly and conveniently realize the self-adapting detecting and correction to the still image random noise of sCMOS camera, have good use value in scientific research camera field.
Description
Technical field
The invention belongs to imaging sensors and field of signal processing, and in particular to be based on a kind of magazine raising of sCMOS
The detection of the random noise of picture quality and the realization of correcting algorithm.
Background technique
SCMOS camera is a to integrate many merits such as low output noise, high sensitivity, high-resolution and high frame frequency
Scientific Grade camera, medical image diagnosis, micro-imaging, astronomical observation and high-definition intelligent monitoring etc. fields suffer from it is important
Effect[1-3].Its high-performance determines that it will be applied to the high field of some pairs of image quality requirements, although this camera
Noise is very low, but works as image and absorbing, during transmitting and converting, (such as: equipment material due to the factors such as inside and outside
Itself limitation, Electromagnetic Interference, environment temperature etc.) presence, picture noise is still very important, this just to the quality of image and
The acquisition of effective component produces influence in image[4,5].In numerous picture noises, random noise is as the most common noise class
One of type due to the rule that it is not fixed, and has very strong randomness, and generally existing in the signal, so, at random
The hot issue for filtering out problem and always being scientific worker's research of noise[6-20]。
Research for random noise, domestic and international researcher have carried out constantly in the detection and bearing calibration of noise
It improves.In classical filter algorithm, multiple image superposition is averaging[6,7]It is the typical method for removing random noise, due to making an uproar at random
Sound is extremely low in the probability that multiple image same point occurs, and after width image averagings up to a hundred, the gray value of random noise will
It is weakened, thus reach calibration result, but this method needs high frame per second camera, is continuously shot a large amount of image, processing speed
Degree will receive limitation.Neighborhood averaging[8]Although algorithm is simple and processing speed is fast, its deficiency is that treated image border
Meeting is imperfect and thickens, and image detail cannot retain well.Buades[9]Et al. propose non-local mean filtering calculate
Method remains image detail, but is only applicable to the image containing Gaussian noise.Median filtering[10-13]It is removal random noise
One of exemplary process, it is theoretical using sequencing statistical, is replaced with sorting value placed in the middle in 8 pixels around object pixel
Original pixel, can effectively inhibit the nonlinear signal processing technology of noise, which is suitable for the elimination of isolated noise spot, makes week
The close true value of the pixel value enclosed.Simplicity and validity based on median filtering, Liu Yang et al.[14,15]It proposes very
More improved methods, such as related weighing median filtering, two-dimensional multistage median filtering etc. make filtering value more reasonable, but simultaneously
Illustrate that the algorithm needs to have higher requirements to testing result.Turkmen et al.[11,12]Pass through setting noise in the noise measuring stage
The method of threshold value carries out median filtering to the noise spot filtered out, has reached preferable calibration result, but the choosing to noise threshold
Foundation is taken not seek unity of standard.
Since the gray value difference of random noise and normal pixel is not very big, so that human eye can not distinguish random in image
The true distribution of noise, so just adding simulation in noiseless standard picture to make noise measuring considerable with calibration result
Random noise, to verify the quality of detection and correcting algorithm.Document[16,17]Be added be typical random noise (such as: Gauss
Noise, salt-pepper noise etc.), document[11-13]With document[18,20]What is be added is the random noise of different densities, and two noise likes are all in ash
Certain rule is followed in degree distribution, their correcting algorithm can be suitable for different noise densities, and calibration result ratio
It is more satisfactory.But for the random noise that camera generates, either noise type or density size, these are all not
Know[11], therefore, to unknown images are carried out with the processing of random noise, need further research.
Aiming at the problem that piece image can not judge the true distribution of random noise, the high frame of sCMOS camera is utilized herein
Rate characteristic proposes the new method about random noise detection under a kind of still image, on the basis that multiple image is averaging
On carried out algorithm improvement, taken statistics to this analysis using Principle of Statistics concentration, amount of images controlled within six width,
So that the correction rate of algorithm is improved, especially suitable for image quality requirements pole under the premise of guaranteeing Detection accuracy
High sCMOS camera.
Bibliography:
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Brilliant and display, 2017,32 (03): 240-248.
[2] design [J] of He Shuwen, Wang Yanjie, Sun Honghai, Zhang Lei, Wu Pei high dynamic Scientific Grade CMOS camera system
Liquid crystal and display, 2015,30 (04): 729-735.
[3]Jin Li.A highly reliable and super-speed optical fiber
transmission for hyper-spectral SCMOS camera[J].Optik-International Journal
For Light and Electron Optics, 2016,127 (3)
[4] median filtering in Zhang Yannan image procossing and its improvement [J] China new traffic, 2018,20 (02): 230-
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[5]Junichi Nakamura.Image Sensors and Signal Processing for Digital
Still Cameras [M] .Taylor and Francis:2005.66-69.
[6] a kind of estimation of digital picture random noise of Li Zhanshu, Ye Haixia, Xu Baiqing and the realization for utilizing MATLAB
[J] equipment manufacturing technology, 2009 (06): 1-2+24.
[7] removal [J] the health care of Dong Ge, Luo Shouhua, Chen Gong .Micro CT projected image noise is equipped, and 2009,
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(06): 17-18.
[9] non-local mean Denoising Algorithm [J] fault block oil gas of Huang Ying, Wen Xiaotao, He Zhenhua seismic image random noise
Field, 2013,20 (06): 730-732.
[10] method [A] China's image graphics of a kind of removal gray scale of Chen Jing and color image random noise learns
14th national image graphics academic meeting paper collection [C] China's image graphics association:, 2008:5.
[11] Guo Yuanhua, Zhou Xianlin detect [J] computer section based on gray-scale intensity and the random impulsive noise in four directions
It learns, 2016,43 (S2): 220-222.
[12]Ilke Turkmen.A new method to remove random-valued impulse noise
In images [J] .AEUE-International Journal of Electronics and Communications,
2013,67 (9)
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impulse noise removal using adaptive dual threshold median filter[J].Journal
Of Visual Communication and Image Representation, 2015,26.
[14] Liu Yang, Wang Dian, Liu Cai, Feng's Xuan local correlation Weighted median filtering technology and its decline in poststack random noise
Application [J] Chinese Journal of Geophysics in subtracting, 2011,54 (02): 358-367.
[15] Wang Mingchang, Xing Lixin, Lv Fengjun, Pan Jun, Meng Tao, Liu Zhihui are removed distant using two-dimensional multistage median filtering
Feel image random noise [J] Jilin University journal (geoscience version), 2004 (S1): 178-180.
[16] Jian Pan, Xinhua Yang, Huafeng Cai, Bingxian Mu.Image noise
Smoothing using a modified Kalman filter [J] .Neurocomputing, 2016,173.
[17] Yingyue Zhou, Zhongfu Ye, Yao Xiao.A restoration algorithm for
images contaminated by mixed Gaussian plus random-valued impulse noise[J]
.Journal of Visual Communication and Image Representation, 2013,24 (3)
[18] Random Valued Impulse Noise of Guo Hongwei, Yujiang County, Luo Hongjun, Zhang Zihong self adaptive control iteration filter [J]
Computer engineering and application, 2011,47 (34): 193-195.
[19] Liu Ce, Szeliski Richard, Bing Kang Sing, Zitnick C Lawrence, Freeman
William T.Automatic estimation and removal of noise from a single image.[J]
.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007,30 (2)
[20] Xia Lan, Zhiyong Zuo.Random-valued impulse noise removal by the
adaptive switching median detectors and detail-preserving regularization[J]
.Optik-International Journal for Light and Electron Optics, 2014,125 (3)
[21] Ji little Jun, Shi Wenkang, Zhang Zijia wait sound Sources Detection of the based on noise signal and fault diagnosis [J] to calculate
Machine measurement and control, 2003,11 (12): 918-920.
[22] EMVA Standard 1288:Standard for Characterization of Image Sensors
And Cameras [S] .Release 3.0,2010.11.
Summary of the invention
The present invention is to propose the self-adapting detecting and bearing calibration of a kind of random noise based on sCMOS camera, it is therefore an objective to
Picture quality is improved, solves the problems, such as that random noise causes corrupt to the image of sCMOS camera.
The technical solution adopted in the present invention specifically includes following operating procedure:
Step 1, camera lens are fixed, adjustment camera properties parameter, continuous acquisition 6 width still image P1, P2, P3, P4,
P5, P6；
Step 2, image P1 and the gray value of P2 corresponding pixel points are made poor, obtain the differential chart D12 of P1 and P2, difference is not
Zero pixel, the as doubtful noise spot of the whole in two images；
Step 3, the method for repeating step 2 to image P1 and P3, P1 and P4, P1 and P5, P1 and P6 respectively, can be obtained difference
Scheme D13, D14, D15 and D16；
Step 4, calculate D12, D13, D14, the grey scale pixel value of this five width differential chart of D15, D16, can find out image P1 with
The doubtful noise spot coordinate of the whole of P2, P1 and P3 ..., P1 and P6；
Step 5, if the gray value of this five width differential chart is not zero in same position, image P1 is doubtful in the point
Noise spot exports the doubtful random noise coordinate of image P1；
Step 6, it first correcting algorithm: in five width image of statistical noise figure P2, P3 ..., P6, under same position coordinate, makes an uproar
The frequency and frequency that sound point gray value occurs；
Step 7, when the maximum gray value of the frequency of occurrences only one when: the maximum gray value of the frequency of occurrences is replaced into P1
The gray value of the noise spot in figure；
Step 8, when the maximum gray value of the frequency of occurrences there are two when: if the difference of the two gray values within 10,
The average value of two gray values is then taken to replace the gray value of the noise spot；Otherwise the coordinate is stored in second-order correction matrix
Mlast；
Step 9, second-order correction algorithm: gray correction is carried out to the coordinate points Mlast that first algorithm does not correct, edge is filled out
It fills and median filtering, boundary is filled up by mirror reflection method, execute 3 × 3 median filtering algorithm correction.
The invention has the advantages that practical application shows that the present invention can not destroy original image normal pixel
Under the premise of information, self-adapting detecting and correction are carried out to the random noise that sCMOS camera generates in shooting process, significantly
Picture quality is improved, there is good use value in scientific research camera field.
The technology of the present invention has following advantage:
(1) operation of the present invention is easy, algorithm is simple, and calculation amount is small, execution efficiency is high, speed is fast.
(2) algorithm of the present invention can in sCMOS scientific research grade camera hardware realization, such as by FPGA realization, with
Arithmetic speed is further increased, can also be realized by upper computer software.
(3) present invention is to be corrected in two times according to the position coordinates of the random noise detected, correct big portion for the first time
Divide the random noise point for comparing affirmative, secondary reparation is by the noise spot of erroneous detection, to keep correction result more accurate.
(4) method proposed by the present invention can quickly and conveniently realize to the still image random noise of sCMOS camera from
Detection and correction are adapted to, there is good use value in scientific research camera field.
(5) the present disclosure applies equally to the self-adapting detectings and correction of other models sCMOS camera random noise.
Detailed description of the invention
Fig. 1 is the operational flowchart of the method for the present invention；
Fig. 2 is the image being not used before the random noise correction that the method for the present invention obtains；
Fig. 3 is the image after the random noise correction obtained using the method for the present invention.
Specific embodiment
Describe specific implementation of the invention in detail below in conjunction with drawings and examples.
The integrated operation process of method proposed by the present invention is as shown in Fig. 1, is broken generally into two ranks in the specific implementation
Section is the high frame per second characteristic using sCMOS camera first, devise multiple image mutually referring to come carry out random noise from
Adapt to detection algorithm, hereinafter referred to as random noise detection-phase；Followed by random noise calibration phase, it is tied as obtained by detection
Fruit in two times corrects it, the random noise point of the most of relatively affirmative of first correction, secondary to repair by the noise spot of erroneous detection,
To keep correction result more accurate.
The operating process of random noise detection-phase is as follows:
Step 1, camera lens are fixed, adjustment camera properties parameter, continuous acquisition 6 width still image P1, P2, P3, P4,
P5, P6；
Step 2, image P1 and the gray value of P2 corresponding pixel points are made poor, obtain the differential chart D12 of P1 and P2, difference is not
Zero pixel, the as doubtful noise spot of the whole in two images；
Step 3, the method for repeating step 2 to image P1 and P3, P1 and P4, P1 and P5, P1 and P6 respectively, can be obtained difference
Scheme D13, D14, D15 and D16；
Step 4, calculate D12, D13, D14, the grey scale pixel value of this five width differential chart of D15, D16, can find out image P1 with
The doubtful noise spot coordinate of the whole of P2, P1 and P3 ..., P1 and P6；
Step 5, if the gray value of this five width differential chart is not zero in same position, image P1 is doubtful in the point
Noise spot exports the doubtful random noise coordinate of image P1.
The operating process of random noise calibration phase is as follows:
Step 6, it first correcting algorithm: in five width image of statistical noise figure P2, P3 ..., P6, under same position coordinate, makes an uproar
The frequency and frequency that sound point gray value occurs；
Step 7, when the maximum gray value of the frequency of occurrences only one when: the maximum gray value of the frequency of occurrences is replaced into P1
The gray value of the noise spot in figure；
Step 8, when the maximum gray value of the frequency of occurrences there are two when: if the difference of the two gray values within 10,
The average value of two gray values is then taken to replace the gray value of the noise spot；Otherwise the coordinate is stored in second-order correction matrix
Mlast；
Step 9, second-order correction algorithm: gray correction is carried out to the coordinate points Mlast that first algorithm does not correct, edge is filled out
It fills and median filtering, boundary is filled up by mirror reflection method, execute 3 × 3 median filtering algorithm correction.
Attached drawing 2 is the cell gray level image being not used before the random noise correction that the method for the present invention acquisition obtains, and attached drawing 3 is
Self-adapting correction method proposed by the invention has been used to carry out the image after random noise correction to attached drawing 2.Two figures compare,
Can significantly it find out, the random noise detection and bearing calibration proposed by the invention based on sCMOS camera efficiently solves
Random noise effective information caused by the image of sCMOS camera destroys problem.
Practical application shows that the present invention can quickly and conveniently realize the still image random noise to sCMOS camera
Self-adapting detecting and correction, scientific research camera field have good use value.
Claims (4)
1. a kind of based on the random noise self-adapting detecting of sCMOS camera and bearing calibration, which is characterized in that this method includes such as
Lower step:
Step 1, camera lens are fixed, adjustment camera properties parameter, continuous acquisition 6 width still image P1, P2, P3, P4, P5,
P6；
Step 2, image P1 and the gray value of P2 corresponding pixel points are made poor, obtain the differential chart D12 of P1 and P2, what difference was not zero
Pixel, the as doubtful noise spot of the whole in two images；
Step 3, the method for repeating step 2 to image P1 and P3, P1 and P4, P1 and P5, P1 and P6 respectively, can be obtained differential chart
D13, D14, D15 and D16；
Step 4, D12, D13, D14 are calculated, the grey scale pixel value of this five width differential chart of D15, D16 can find out image P1 and P2, P1
With the doubtful noise spot coordinate of whole of P3 ..., P1 and P6；
Step 5, if the gray value of this five width differential chart is not zero in same position, image P1 is doubtful noise in the point
Point exports the doubtful random noise coordinate of image P1；
Step 6, first correcting algorithm: in five width image of statistical noise figure P2, P3 ..., P6, under same position coordinate, noise spot
The frequency and frequency that gray value occurs；
Step 7, when the maximum gray value of the frequency of occurrences only one when: the maximum gray value of the frequency of occurrences is replaced in P1 figure
The gray value of the noise spot；
Step 8, when the maximum gray value of the frequency of occurrences there are two when: if the difference of the two gray values takes within 10
The average value of two gray values replaces the gray value of the noise spot；Otherwise the coordinate is stored in second-order correction matrix Mlast；
Step 9, second-order correction algorithm: the coordinate points Mlast not corrected to first algorithm carries out gray correction, edge filling with
Median filtering, boundary are filled up by mirror reflection method, execute 3 × 3 median filtering algorithm correction.
2. as described in claim 1 a kind of based on the random noise self-adapting detecting of sCMOS camera and bearing calibration, feature
Be, in step 2 the doubtful noise point methods of whole in the determination two images: the gray value of two width continuous shooting images is made
Difference, the pixel that difference is not zero as have coordinate points existing for doubtful noise.
3. as described in claim 1 a kind of based on the random noise self-adapting detecting of sCMOS camera and bearing calibration, feature
It is, in step 4 the method for the determination random noise: random in piece image to determine by comparing 5 width differential charts
The final coordinate of noise.
4. as described in claim 1 a kind of based on the random noise self-adapting detecting of sCMOS camera and bearing calibration, feature
It is, the random noise described in step 7 and step 8 corrects for the first time, occurs in multiple image by counting the noise spot
Gray value size and frequency, to replace the gray value of the noise spot, correction result is more accurate.
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| CN114051127A (en) * | 2022-01-14 | 2022-02-15 | 深圳市艾科维达科技有限公司 | Image transmission noise reduction method of network set top box |
| CN116228589A (en) * | 2023-03-22 | 2023-06-06 | 新创碳谷集团有限公司 | Method, equipment and storage medium for eliminating noise points of visual inspection camera |
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