CN100508556C - Grid noise detection and elimination device of the digital image and its method - Google Patents
Grid noise detection and elimination device of the digital image and its method Download PDFInfo
- Publication number
- CN100508556C CN100508556C CNB2007100985855A CN200710098585A CN100508556C CN 100508556 C CN100508556 C CN 100508556C CN B2007100985855 A CNB2007100985855 A CN B2007100985855A CN 200710098585 A CN200710098585 A CN 200710098585A CN 100508556 C CN100508556 C CN 100508556C
- Authority
- CN
- China
- Prior art keywords
- detected
- unit
- noise
- pixel
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000001514 detection method Methods 0.000 title claims description 16
- 230000008030 elimination Effects 0.000 title abstract description 6
- 238000003379 elimination reaction Methods 0.000 title abstract description 6
- 239000000470 constituent Substances 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 description 8
- 230000003287 optical effect Effects 0.000 description 8
- 238000004519 manufacturing process Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000000205 computational method Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Landscapes
- Image Processing (AREA)
- Color Television Image Signal Generators (AREA)
Abstract
A digital image grid shape walk away and the elimination method, contain the step: The computation treats examines the Bayer picture to treat examines in the region respectively to treat the examination unit the unit noise parameter; The statistics is bigger than the hypothesis the first threshold value unit noise parameter integer: Cont; Carries on the judgments to the Cont value, if Cont is bigger than the hypothesis the second threshold value, then determines should treat examines the Bayer picture existence grid shape noise, otherwise determines should treat examines the Bayer picture not to have the grid shape noise; If determines should treat examines the Bayer picture existence grid shape noise, then to should treat examines in the Bayer picture to treat disappears in noise region each G to pixel carries on the following operation: Designated G pixel, takes this picture pixel and its neighboring 4 G pixel the initial G value weighted average achievement to pass through the noise eliminates should pixel the G value. This invention fast conveniently improved the picture quality by the low cost way, enhanced the product quality.
Description
Technical field
The present invention relates to the detection and the elimination of the latticed noise of digitized video, relate in particular to a kind of latticed noise measuring and cancellation element and detection and removing method of digitized video.
Background technology
The optical pickocff of digital image pick-ups such as camera, digital camera and Digital Video adopts CCD (Charge-coupled device usually, charge coupled device) or CMOS (ComplementaryMetal Oxide Semiconductor, complementary matal-oxide semiconductor) technology, a two-dimensional matrix of forming by the photo-sensitive cell of both direction dense arrangement (CCD or CMOS) anyhow, and CCD or cmos sensor can only be responded to light luminance, can not respond to color information.Therefore (Color Filtered Array CFA) guarantees that each sensor pixel only can receive a kind of light of color: a kind of in normally red (R), green (G), blue (B) three kinds of colors must to use the color filter array.The color filter array can use different patterns, and the most frequently used is the color filter array of Bayer (Bel) pattern.The Bayer pattern is used alternatingly one group of red and green filter and one group of green and blue filter, and wherein green pixel point adds up to redness and blue pixel point sum.The line of pixels column format of original (raw) image (hereinafter to be referred as the Bayer image) of Bayer pattern as shown in Figure 1.Image for the Bayer image transitions is become can normally show need carry out cfa interpolation to it, promptly to each pixel, obtains other two kinds of color values of this pixel by the pixel value around it.For example, [m, n] locates in the position, has only the G value, utilizes the information of point on every side, can obtain the R value and the B value at this some place by interpolation.Through behind the cfa interpolation, can obtain R, G, B value on each pixel.
Because general natural scene image all is smooth, that is to say that color is slow gradual change, therefore, the G value (diagonal angle is adjacent) that the Bayer image is adjacent should be very approaching.But, in the production process of digital image pick-up, because the production technology assembling alignment precision relatively poor or camera lens and optical pickocff of optical pickocff is not enough, make the Bayer image that generates at optical pickocff the differ greatly phenomenon of (being G value imbalance) of adjacent G value can occur.And will there be latticed noise in this Bayer image through the image that generates after the cfa interpolation, have a strong impact on picture quality.
Fig. 2 is the schematic diagram of latticed noise, and the left side is not for existing the normal picture of latticed noise, and the right is the image that has latticed noise.
Usually, the detection of latticed noise is adopted carrying out the perusal identification mode through the image behind the cfa interpolation, promptly the uniform object of color (as blank sheet of paper) is taken, and the image through behind the cfa interpolation of taking gained is carried out naked eyes identification.And in order to eliminate latticed noise, then need production technology is improved, with the quality of raising optical pickocff and the alignment precision of camera lens and optical pickocff.But, adopt perusal identification mode detection efficiency very low, be difficult to the image that a large amount of digital image pick-ups is taken is carried out the detection of latticed noise.Often need to drop into a large amount of funds and time and production technology improved, even and production technology carried out improving be difficult to also guarantee that each digital image pick-up of producing can both satisfy quality requirement.
Summary of the invention
Technical problem to be solved by this invention is, overcomes the detection of the latticed noise of prior art and the deficiency of removing method, proposes a kind of method that automatically latticed noise is detected and eliminates according to the Bayer view data.
In order to solve the problems of the technologies described above, the invention provides a kind of latticed noise detecting method of digitized video, comprise following steps:
A) the noise parameter Diff[i of unit of each unit to be detected in the zone to be detected of calculating Bayer image to be detected]=| Mean[i]-Mean ' [i] |; Wherein, Mean[i] be the G pixel mean value of i unit to be detected; Mean ' [i] is the G pixel mean value of the adjacent cells of i unit to be detected; 1≤i≤K, K are the number of the unit to be detected in this zone to be detected;
B) each unit noise parameter is judged that statistics is greater than the number of the unit noise parameter of the first threshold of setting: Cont;
C) the Cont value is judged,, otherwise judged that there is not latticed noise in this Bayer image to be detected if Cont, judges then that there is latticed noise in this Bayer image to be detected greater than second threshold value of setting.
In addition, described zone to be detected is meant the subwindow that the Bayer image division is obtained, or whole Bayer image.
In addition, described unit is the row or column in the described zone to be detected, interval L row or column between the described row or column to be detected, and L is a positive integer.
In addition, the span of described first threshold is: 3～10.
In addition, the span of described second threshold value is: K/2≤second threshold value≤K-1.
The present invention also provides a kind of latticed noise measuring and removing method of digitized video, comprises following steps:
(a) the noise parameter Diff[i of unit of each unit to be detected in the zone to be detected of calculating Bayer image to be detected]=| Mean[i]-Mean ' [i] |; Wherein, Mean[i] be the G pixel mean value of i unit to be detected; Mean ' [i] is the G pixel mean value of the adjacent cells of i unit to be detected; 1≤i≤K, K are the number of the unit to be detected in this zone to be detected;
(b) each unit noise parameter is judged that statistics is greater than the number of the unit noise parameter of the first threshold of setting: Cont;
(c) the Cont value is judged,, otherwise judged that there is not latticed noise in this Bayer image to be detected if Cont, judges then that there is latticed noise in this Bayer image to be detected greater than second threshold value of setting.
(d) if judge that there is latticed noise in this Bayer image to be detected, then each the G pixel in the eliminated noise zone for the treatment of in this Bayer image to be detected is carried out following operation: selected G pixel, the weighted average conduct of initial G value of getting this pixel and adjacent 4 G pixels thereof is through the G value of this pixel of noise removing; Wherein, the weighted value of the G pixel that this is selected is greater than 0, and the weighted value of 4 G pixels that it is adjacent is all more than or equal to 0, and at least one is greater than 0;
Described zone to be detected is meant the subwindow that the Bayer image division is obtained, or whole Bayer image;
The described eliminated noise zone for the treatment of is meant the subwindow that the Bayer image division is obtained, or whole Bayer image;
Described unit is the row or column in the described zone to be detected;
The span of described first threshold is: 3～10;
The span of described second threshold value is: K/2≤second threshold value≤K-1.
The present invention also provides a kind of latticed noise detection apparatus of digitized video, it is characterized in that, this device comprises pixel statistic unit, noise parameter unit and noise judgement unit, wherein:
Described pixel statistic unit is used for adding up each unit to be detected of zone to be detected of Bayer image to be detected and the G pixel statistical value of adjacent cells thereof;
Described noise parameter unit is used for obtaining corresponding G pixel mean value according to the G pixel statistical value of each unit to be detected and adjacent cells thereof, and unit of account noise parameter: Diff[i]=| Mean[i]-Mean ' [i] |, wherein, Mean[i] be the G pixel mean value of i unit to be detected; Mean ' [i] is the G pixel mean value of the adjacent cells of i unit to be detected; 1≤i≤K, K are the number of the unit to be detected in this zone to be detected;
Described noise judgement unit is used for whether differentiating the result greater than the latticed noise that second threshold value generates Bayer image to be detected according to the constituent parts noise parameter greater than the number of the first threshold of setting.
In addition, described zone to be detected is meant the subwindow that the Bayer image division is obtained, or whole Bayer image; Described unit is the row or column in the described zone to be detected; The span of described first threshold is: 3～10; The span of described second threshold value is: K/2≤second threshold value≤K-1.
The present invention also provides a kind of latticed noise measuring and cancellation element of digitized video, it is characterized in that, this device comprises pixel statistic unit, noise parameter unit, noise judgement unit, weight parameter unit and weighted average unit, wherein:
Described pixel statistic unit is used for adding up each unit to be detected of zone to be detected of Bayer image to be detected and the G pixel statistical value of adjacent cells thereof;
Described noise parameter unit is used for obtaining corresponding G pixel mean value according to the G pixel statistical value of each unit to be detected and adjacent cells thereof, and unit of account noise parameter: Diff[i]=| Mean[i]-Mean ' [i] |, wherein, Mean[i] be the G pixel mean value of i unit to be detected; Mean ' [i] is the G pixel mean value of the adjacent cells of i unit to be detected; 1≤i≤K, K are the number of the unit to be detected in this zone to be detected;
Described noise judgement unit is used for whether differentiating the result greater than the latticed noise that second threshold value generates Bayer image to be detected according to the constituent parts noise parameter greater than the number of the first threshold of setting;
Described weighted average unit is used to use weighted value that each the G pixel of Bayer view data and the initial G value of adjacent G pixel thereof are weighted on average, generates the G value through each G pixel of noise removing;
Described weight parameter unit is used to preserve and be provided with the required weighted value parameter in weighted average unit;
Described zone to be detected is meant the subwindow that the Bayer image division is obtained, or whole Bayer image; Described unit is the row or column in the described zone to be detected; The span of described first threshold is: 3～10; The span of described second threshold value is: K/2≤second threshold value≤K-1.
The present invention is by carrying out latticed noise measuring automatically to the Bayer image before cfa interpolation, and adopts weighted-average method to eliminate latticed noise, improved the quality of image quickly and easily in mode cheaply, improved the quality of product.
Description of drawings
Fig. 1 is the line of pixels column format schematic diagram of the original image of Bayer pattern;
Fig. 2 is the schematic diagram of latticed noise;
Fig. 3 is the latticed noise measuring of embodiment of the invention digitized video and the flow chart of removing method;
Fig. 4 is the latticed noise measuring of embodiment of the invention digitized video and the structure chart of cancellation element.
Embodiment
Cause because latticed noise is a G value imbalance, therefore can the G value of adjacent row or column be compared, whether have the unbalanced phenomenon of G value to detect; In order to eliminate latticed noise, the G value pixel that each original G value pixel can be adjacent is weighted on average, and with the G value of this weighted average as this pixel.
Below in conjunction with drawings and Examples the present invention is described in detail.
Because latticed generating noise is because the production technology of optical pickocff or the assembling alignment precision of camera lens and optical pickocff cause inadequately, therefore has consistency in each zone of image, even detect latticed noise, can think usually that then there is latticed noise in this image in certain enough big zone of image.So,, can carry out latticed noise measuring in one or more zones of image in order to reduce amount of calculation.Certainly, the related pixel of latticed noise measuring is many more, and accuracy of detection is just high more.
As shown in Figure 3, the Bayer image is carried out latticed noise measuring and comprises following steps:
101: the capable mean value Gmean[m that calculates each the row G pixel in the zone to be detected of image to be detected], wherein, 1≤m≤M, M are the line number of pixel in the zone to be detected;
Above-mentioned image to be detected can be the uniform standard picture of the captured color of digital image pick-up (for example, blank sheet of paper being taken the image that is obtained), standard picture is carried out noise measuring can obtain the higher detection precision.The testing result that standard picture has been done can be used as the foundation whether this digital image pick-up needs image is carried out latticed noise cancellation operation.Can certainly each width of cloth image that digital imaging device is taken be detected.
Notice, calculating Gmean[m] time, if participating in asking the number of the G pixel of average calculating operation in each row is 2 x power, then can adopt gt computing (the x position moves to right) to replace division arithmetic, therefore can accelerate arithmetic speed greatly, when selecting zone to be detected, can consider every row is comprised the zone of an x power G pixel of 2 as zone to be detected.
102: capable from the 1st row to M-1, calculate the capable noise parameter that each is gone:
Gdiff[m]=| Gmean[m]-Gmean[m+1] |, 1≤m≤M-1 wherein;
Above-mentioned formula is with the absolute value of the difference of the capable mean value of the G pixel of current line and next adjacent lines capable noise parameter as this current line, and the absolute value of difference of capable mean value of G pixel that also can get current line and last adjacent lines is as the capable noise parameter of this current line.
103: each row noise parameter is judged that statistics is greater than the number Gcont of the capable noise parameter of the first threshold TH_diff that sets;
The span of above-mentioned first threshold TH_diff is: 3≤TH_diff≤10; Usually get TH_diff=5.
104: the Gcont value is judged greater than the second threshold value TH_count that sets, then process decision chart looks like to exist latticed noise as if Gcont, otherwise there is not latticed noise in the process decision chart picture; Export above-mentioned latticed noise result of determination.
The span of the above-mentioned second threshold value TH_count is: M/2≤TH_count≤M-1, get TH_count=0.8 * M usually.
Above-mentioned latticed noise result of determination exports noise elimination apparatus to, if according to these result of determination needs this image is carried out noise removing, then can carry out noise cancellation operation to the G pixel of entire image.Certainly,, also can only carry out noise cancellation operation, for example, only main body (prospect) part of image be carried out noise removing some or a plurality of zones of image in order to reduce amount of calculation.As shown in Figure 3, image is carried out noise removing and comprises following steps:
201: treat each the G pixel in the eliminated noise zone, the weighted average of initial G value of getting this pixel and adjacent 4 G pixels thereof is as the G value through this pixel of noise removing, that is:
G_Out[i, j]=(a * G[i-1, j-1]+b * G[i-1, j+1]+c * G[i+1, j-1]+d * G[i+1, j+1]+e * G[i, j])/(a+b+c+d+e); (formula 1)
Wherein, above-mentioned G_Out[i, j] expression is through the G value of the pixel [i, j] of noise removing;
G[x, y] the original G value of remarked pixel point [x, y], x=i, i-1, i+1, y=j, j-1, j+1;
Above-mentioned 1≤i≤M, 1≤j≤N, and pixel [i, j] treating in the eliminated noise zone at image;
Above-mentioned a, b, c, d, e are weighted value, and a, b, c, d are the real number more than or equal to 0, e is the real number greater than 0.
As one of preferred embodiment, desirable: one among e=1 and a, b, c, the d is 1, and other is 0, for example: get a=e=1, b=c=d=0:
G_Out[i，j]＝(G[i-1，j-1]+G[i，j])/2；
Or get: d=e=1, a=b=c=0, that is:
G_Out[i，j]＝(G[i+1，j+1]+G[i，j])/2；
During specific implementation, be to accelerate arithmetic speed, replace division arithmetic in above-mentioned 2 formula with dextroposition usually, above-mentioned formula becomes:
G_Out[i, j]=(G[i-1, j-1]+G[i, j]) 1; Or
G_Out[i，j]＝(G[i+1，j+1]+G[i，j])<<1。
As another preferred embodiment, desirable a, b, c, d, e are the real number greater than 0, a wherein, b, c, d are all less than e, for example: a=b=c=d=1, e=4, that is:
G_Out[i，j]＝(G[i-1，j-1]+G[i-1，j+1]+G[i+1，j-1]+G[i+1，j+1]+4×G[i，j])/8。
Equally, during specific implementation, replace division arithmetic with dextroposition, the replacement multiplying of shifting left, above-mentioned formula becomes:
G_Out[i，j]＝(G[i-1，j-1]+G[i-1，j+1]+G[i+1，j-1]+G[i+1，j+1]+G[i，j]<<2)>>3。
In the various embodiments described above, carry out noise removing when handling at boundary point to image, if do not have formula 1 required [i-1, j-1], [i-1, j+1], the one or more adjacent G pixel in [i+1, j-1] and [i+1, j+1], desirable corresponding weights are 0.
202: will export the cfa interpolation unit to through the Bayer view data that noise removing is handled and carry out interpolation processing.
As shown in Figure 4, the latticed noise detection apparatus of digitized video comprises pixel statistic unit, noise parameter unit and noise judgement unit.Wherein:
The pixel statistic unit is used to add up the capable statistical value of G pixel of Bayer image; The capable statistical value of G pixel is the accumulated value of the G value of the G pixel that comprises in the delegation.
The noise parameter unit is used for obtaining the capable mean value of G pixel according to the capable statistical value of G pixel, and uses the formula in the above-mentioned steps 102 to calculate the row noise parameter.
The noise judgement unit, be used for differentiating the result according to the latticed noise of row noise parameter and first threshold and second threshold value generation Bayer image, even in each row noise parameter greater than the number of first threshold greater than second threshold value, then process decision chart looks like to exist latticed noise, otherwise there is not latticed noise in the process decision chart picture.The span of the first threshold and second threshold value is the same.
The latticed noise elimination apparatus of digitized video comprises weight parameter unit and weighted average unit.Wherein:
The weighted average unit is used to use weighted value that each the G pixel of Bayer view data and the initial G value of adjacent G pixel thereof are weighted on average, generates the G value through each G pixel of noise removing; Computing formula is seen (formula 1).
The weight parameter unit is used to preserve and be provided with the required weighted value in weighted average unit: a, b, c, d and e.Weighted value in the weight parameter unit can be provided with dynamic adjustment according to current system resource behaviour in service or user.When system resource was nervous, 3 weighted values that can be provided with among a, b, c, the d were 0, promptly only used 1 adjacent G pixel to participate in the weighted average computing.
Based on design of the present invention, on the basis of the foregoing description, can also do with down conversion:
For example, in zone to be detected, can select the part G pixel point value participation in every row to ask average calculating operation, to obtain the capable mean value of G pixel; The row noise parameter neither calculate each row, can calculate every L is capable, general L span is 1～4, but also can get other value, certainly, the row that still need use row to be detected to be adjacent when calculating the row noise parameter calculates, and correspondingly the span of the second threshold value TH_count becomes: K/2≤TH_count≤K-1, wherein K is the number of row noise parameter.
In addition, when image is carried out noise measuring, also can classify detection unit as, promptly calculate the column average value of the G pixel of each row in the zone to be detected earlier, and finally obtaining testing result according to the corresponding row noise parameter of each row that calculates, computational methods are similar to step 102.
In sum, the detection of latticed noise of the present invention and elimination are to carry out on the basis of Bayer image, have filled up the blank of prior art association area, and a kind of directly detection and the removing method of effective latticed noise are provided.
Claims (9)
1, a kind of latticed noise detecting method of digitized video comprises following steps:
A) the noise parameter Diff[i of unit of each unit to be detected in the zone to be detected of calculating Bayer image to be detected]=IMean[i]-Mean ' [i] |; Wherein, Mean[i] be the G pixel mean value of i unit to be detected; Mean ' [i] is the G pixel mean value of the adjacent cells of i unit to be detected; 1≤i≤K, K are the number of the unit to be detected in this zone to be detected;
B) each unit noise parameter is judged that statistics is greater than the number of the unit noise parameter of the first threshold of setting: Cont;
C) the Cont value is judged,, otherwise judged that there is not latticed noise in this Bayer image to be detected if Cont, judges then that there is latticed noise in this Bayer image to be detected greater than second threshold value of setting.
2, the latticed noise detecting method of digitized video as claimed in claim 1 is characterized in that, described zone to be detected is meant the subwindow that the Bayer image division is obtained, or whole Bayer image.
3, the latticed noise detecting method of digitized video as claimed in claim 1, it is characterized in that, described unit is the row or column in the described zone to be detected, L row in interval between the row between the row in the described zone to be detected in the capable or described zone to be detected of L, interval, and L is a positive integer.
4, the latticed noise detecting method of digitized video as claimed in claim 1 is characterized in that, the span of described first threshold is: 3～10.
5, the latticed noise detecting method of digitized video as claimed in claim 1 is characterized in that, the span of described second threshold value is: K/2≤second threshold value≤K-1.
6, a kind of latticed noise measuring and removing method of digitized video comprise following steps:
(a) the noise parameter Diff[i of unit of each unit to be detected in the zone to be detected of calculating Bayer image to be detected]=| Mean[i]-Mean ' [i] |; Wherein, Mean[i] be the G pixel mean value of i unit to be detected; Mean ' [i] is the G pixel mean value of the adjacent cells of i unit to be detected; 1≤i≤K, K are the number of the unit to be detected in this zone to be detected;
(b) each unit noise parameter is judged that statistics is greater than the number of the unit noise parameter of the first threshold of setting: Cont;
(c) the Cont value is judged,, otherwise judged that there is not latticed noise in this Bayer image to be detected if Cont, judges then that there is latticed noise in this Bayer image to be detected greater than second threshold value of setting;
(d) if judge that there is latticed noise in this Bayer image to be detected, then each the G pixel in the eliminated noise zone for the treatment of in this Bayer image to be detected is carried out following operation: selected G pixel, the weighted average conduct of initial G value of getting this pixel and adjacent 4 G pixels thereof is through the G value of this pixel of noise removing; Wherein, the weighted value of the G pixel that this is selected is greater than 0, and the weighted value of 4 G pixels that it is adjacent is all more than or equal to 0, and at least one is greater than 0;
Described zone to be detected is meant the subwindow that the Bayer image division is obtained, or whole Bayer image;
The described eliminated noise zone for the treatment of is meant the subwindow that the Bayer image division is obtained, or whole Bayer image;
Described unit is the row or column in the described zone to be detected;
The span of described first threshold is: 3～10;
The span of described second threshold value is: K/2≤second threshold value≤K-1.
7, a kind of latticed noise detection apparatus of digitized video is characterized in that, this device comprises pixel statistic unit, noise parameter unit and noise judgement unit, wherein:
Described pixel statistic unit is used for adding up each unit to be detected of zone to be detected of Bayer image to be detected and the G pixel statistical value of adjacent cells thereof;
Described noise parameter unit is used for obtaining corresponding G pixel mean value according to the G pixel statistical value of each unit to be detected and adjacent cells thereof, and unit of account noise parameter: Diff[i]=| Mean[i]-Mean ' [i] |, wherein, Mean[i] be the G pixel mean value of i unit to be detected; Mean ' [i] is the G pixel mean value of the adjacent cells of i unit to be detected; 1≤i≤K, K are the number of the unit to be detected in this zone to be detected;
Described noise judgement unit is used for whether differentiating the result greater than the latticed noise that second threshold value generates Bayer image to be detected according to the constituent parts noise parameter greater than the number of the first threshold of setting.
8, latticed noise detection apparatus as claimed in claim 7 is characterized in that, described zone to be detected is meant the subwindow that the Bayer image division is obtained, or whole Bayer image; Described unit is the row or column in the described zone to be detected; The span of described first threshold is: 3～10; The span of described second threshold value is: K/2≤second threshold value≤K-1.
9, a kind of latticed noise measuring and cancellation element of digitized video is characterized in that, this device comprises pixel statistic unit, noise parameter unit, noise judgement unit, weight parameter unit and weighted average unit, wherein:
Described pixel statistic unit is used for adding up each unit to be detected of zone to be detected of Bayer image to be detected and the G pixel statistical value of adjacent cells thereof;
Described noise parameter unit is used for obtaining corresponding G pixel mean value according to the G pixel statistical value of each unit to be detected and adjacent cells thereof, and unit of account noise parameter: Diff[i]=| Mean[i]-Mean ' [i] |, wherein, Mean[i] be the G pixel mean value of i unit to be detected; Mean ' [i] is the G pixel mean value of the adjacent cells of i unit to be detected; 1≤i≤K, K are the number of the unit to be detected in this zone to be detected;
Described noise judgement unit is used for whether differentiating the result greater than the latticed noise that second threshold value generates Bayer image to be detected according to the constituent parts noise parameter greater than the number of the first threshold of setting;
Described weighted average unit is used to use weighted value that each the G pixel of Bayer view data and the initial G value of adjacent G pixel thereof are weighted on average, generates the G value through each G pixel of noise removing;
Described weight parameter unit is used to preserve and be provided with the required weighted value parameter in weighted average unit;
Described zone to be detected is meant the subwindow that the Bayer image division is obtained, or whole Bayer image; Described unit is the row or column in the described zone to be detected; The span of described first threshold is: 3～10; The span of described second threshold value is: K/2≤second threshold value≤K-1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2007100985855A CN100508556C (en) | 2007-04-20 | 2007-04-20 | Grid noise detection and elimination device of the digital image and its method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2007100985855A CN100508556C (en) | 2007-04-20 | 2007-04-20 | Grid noise detection and elimination device of the digital image and its method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101035197A CN101035197A (en) | 2007-09-12 |
| CN100508556C true CN100508556C (en) | 2009-07-01 |
Family
ID=38731474
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB2007100985855A Expired - Fee Related CN100508556C (en) | 2007-04-20 | 2007-04-20 | Grid noise detection and elimination device of the digital image and its method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN100508556C (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2197214A4 (en) * | 2007-10-03 | 2010-12-29 | Panasonic Corp | Method and device for reducing block distortion |
| CN102752625A (en) * | 2012-06-19 | 2012-10-24 | 广东欧珀移动通信有限公司 | Method for testing dark current noise of camera module |
| IN2013MN02138A (en) | 2012-08-07 | 2015-06-12 | Spreadtrum Comm Shanghai Co | |
| CN106095732B (en) * | 2016-06-08 | 2018-08-24 | 北京普杰生物技术有限公司 | A kind of noise reduction process method and system suitable for experimental data |
| CN107018343B (en) * | 2017-03-15 | 2020-07-17 | 珠海全志科技股份有限公司 | Bayer image green channel balancing method and device |
| CN117422713B (en) * | 2023-12-18 | 2024-05-14 | 武汉光谷航天三江激光产业技术研究院有限公司 | Method and system for extracting laser welding penetration curve according to OCT point cloud |
-
2007
- 2007-04-20 CN CNB2007100985855A patent/CN100508556C/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN101035197A (en) | 2007-09-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN100508556C (en) | Grid noise detection and elimination device of the digital image and its method | |
| CN101272503B (en) | Gridding noise elimination method and device for remaining image definition | |
| US7995868B2 (en) | Image processing method and image processing device | |
| CN106210712B (en) | A kind of dead pixel points of images detection and processing method | |
| JP4054184B2 (en) | Defective pixel correction device | |
| KR101114586B1 (en) | Apparatus and method for removing of defect pixel | |
| CN106488203B (en) | Image processing method, image processing apparatus, imaging device and electronic installation | |
| CN106604001B (en) | Image processing method, image processing apparatus, imaging device and electronic device | |
| US7671909B2 (en) | Method and apparatus for processing Bayer-pattern digital color video signal | |
| CN103444183B (en) | Color imaging element and imaging device | |
| CN103139492B (en) | Camera head, image capture method and the system of supervision | |
| US9030579B2 (en) | Image processing apparatus and control method that corrects a signal level of a defective pixel | |
| TW200816791A (en) | Image processing apparatus and method for reducing noise in image signal | |
| CN109040720B (en) | A kind of method and device generating RGB image | |
| TW201813371A (en) | Ghost artifact removal system and method | |
| TWI449027B (en) | Adaptive pixel compensation method | |
| CN103460703B (en) | Color image capturing element and image capturing device | |
| CN102224524B (en) | Image processing device and image processing method | |
| US20100231758A1 (en) | Image processing apparatus, image processing method and computer program | |
| CN100589581C (en) | An interpolation method and device for color filter array | |
| CN107644437B (en) | Color cast detection system and method based on blocks | |
| CN102204239B (en) | Single row based defective pixel correction | |
| JP2005012479A (en) | Data processor, image processor, camera, and data processing method | |
| CN104025581B (en) | Imaging Device | |
| CN101060643B (en) | An adaptive bad point removing method for digital shooting of camera |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20090701 Termination date: 20200420 |