CN101242542B - An image detection method and device - Google Patents
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Abstract
The present invention provides an image detecting method, including:collecting multiple original images to be detected, overlapping the multiple original images to be detected; dividing the overlapping image into multiple small image blocks, selecting at least small image block meeting condition as reference area from multiple small image blocks; processing image detection according to the reference area. At the same time, the inventin also provides an image detector. The embodiment of the invention detects deads from global view on the basis of the original images, improving precision of image deads detection, reducing calculated amount.
Description
Technical field
The present invention relates to dead pixel points of images detection technique field, relate in particular to a kind of image detecting method and device.
Background technology
It substantially all is Bel (Bayer) type that the pixel of the imageing sensor (Sensor) of now general digital product is arranged, and each point has only a color component on the image of Bayer type, as shown in Figure 1.Need through color filter array interpolation (Color Filter Array Interpolation, cfa interpolation) or after going mosaic (Demosaicking) to handle on each point, to obtain RGB (Red, Green, Blue, RGB) three color components, obtain a coloured image displayable, that human eye can be seen thus.
Because the manufacturing technology level of moment sensor is limit, and bad point (Dead Pixel) may occur.Wherein, bad point or defect pixel point are not meant and change with photosensitivity, present the pixel of unified brightness all the time, show as bright spot or dim spot.
If uncompensation falls bad point, behind cfa interpolation, these bad points can show as stain, bright spot or color point so; Carry out interpolation owing to using contiguous several pixels in the cfa interpolation process on every side simultaneously,, thereby have a strong impact on picture quality so a bad point can influence a plurality of pixels.
Therefore, at first need the correct bad point that detects to compensate, go bad the method that a little detects in the prior art and show as:
On the RGB image behind the cfa interpolation, even the YUV image after the conversion of RGB process detects bad point, but bad point spreads on the RGB image behind the cfa interpolation, variation has taken place in the coordinate of bad point, and the characteristic of bad point changes to some extent behind the cfa interpolation, if pass through other image processing step again, the possibility wrong for the detection appearance of bad point is very big.And, handling on the RGB image behind the cfa interpolation or on the YUV image, because it is will consider three pixel component on each pixel, therefore big than computational processing on original Bayer image.
And, when detecting bad point,, just in the neighborhood scope of a part, compare and judge for the current point that will detect.When with the neighborhood of certain pixel comparison in when bad point is also arranged, testing result is mistake probably, the precision of detection is low.That is, adopting the method relatively of judging in the local neighborhood, can only detect single bad point, is not to be suitable for for detecting a plurality of bad points.
Summary of the invention
The embodiment of the invention provides a kind of image detecting method and device, in order to solve the problem that prior art dead pixel points of images accuracy of detection is low, amount of calculation is big.
A kind of image detecting method that the embodiment of the invention provides comprises:
Gather several original images to be detected,, ask the mean value of this additive value,, obtain superimposed image the gray value of this mean value as corresponding pixel points with the gray value addition of described several original image corresponding pixel points to be detected;
Superimposed image is divided, be divided into a plurality of little image blocks, calculate the mean value of gray value of described a plurality of little image blocks and the variance of gray value, the variance of described gray value and the variance threshold values of setting are compared, when the variance of the gray value of little image block during less than the variance threshold values set, with described little image block as reference area;
Ask the mean value of the gray value of described reference area, each gray values of pixel points of compensation back image and the difference and the difference threshold of described mean value are compared;
When the absolute value of the gray values of pixel points of compensation back image and the difference of described mean value during, judge that this pixel is a normal point less than the difference threshold of setting;
When the absolute value of the gray values of pixel points of compensation back image and the difference of described mean value during, judge that this pixel is a bad point greater than the difference threshold of setting.
A kind of image detection device that the embodiment of the invention provides comprises:
Gather laminating module, be used to gather several original images to be detected,, ask the mean value of this additive value,, obtain superimposed image the gray value of this mean value as corresponding pixel points with the gray value addition of described several original image corresponding pixel points to be detected;
Divide module, be used for superimposed image is divided, be divided into a plurality of little image blocks;
Choose module, be used to calculate the mean value of gray value of described a plurality of little image blocks and the side of gray value
Difference compares the variance yields of described gray value and the variance threshold values of setting, when the variance yields of the gray value of little image block during less than the variance threshold values set, with described little image block as reference area;
Detection module, be used to ask the mean value of the gray value of described reference area, each gray values of pixel points of compensation back image and the difference and the difference threshold of described mean value are compared, when the absolute value of the gray values of pixel points of compensation back image and the difference of described mean value during less than the difference threshold of setting, judge that this pixel is a normal point, when the absolute value of the gray values of pixel points of compensation back image and the difference of described mean value during, judge that this pixel is a bad point greater than the difference threshold of setting.
The embodiment of the invention is when carrying out the dead pixel points of images detection, and the viewpoint from the overall situation on the basis of original image detects bad point, and several original images are superposeed, and the image after the stack is compensated, and has improved the signal to noise ratio and the stability of image.Therefore, adopt the embodiment of the invention can reduce amount of calculation, the raising accuracy of detection of dead pixel points of images detection.
Description of drawings
Fig. 1 is Bel's picture format figure in the prior art;
The dead pixel points of images detection method flow chart that Fig. 2 provides for the embodiment of the invention;
Fig. 3 is an offset lens image non-uniformity method flow diagram in the prior art;
Fig. 4 is the image division schematic diagram in the embodiment of the invention;
Fig. 5 is the reference area schematic diagram in the embodiment of the invention;
The schematic diagram of a kind of image detection device that Fig. 6 provides for the embodiment of the invention.
Embodiment
In embodiments of the present invention, employing is handled on original image, treat bad point from the angle of integral body, at first gathering several original images to be detected superposes and compensates, compensating images is divided into a plurality of little image blocks, from a plurality of little image blocks, choose at least one little image block that meets the demands as reference area, obtain the fiducial value of pixel gray value according to reference area, with the compensation after image each gray values of pixel points therewith the fiducial value of gray value compare, what satisfy condition regards as normal point, otherwise, regard as bad point.
Wherein, when obtaining the fiducial value of gray values of pixel points, for signal to noise ratio and the stability that improves image, general several original images to be detected of gathering superpose, wherein Die Jia process comprises, the mean value of gray value is asked in the gray value addition of several original image corresponding pixel points to be detected, this mean value as each gray values of pixel points, is obtained superimposed image;
Because the restriction of sampling instrument and the characteristics of original image to be detected, need carry out uniformity compensation to superimposed image, wherein, the method for uniformity compensation comprises that offset lens image non-uniformity parameter and correcting image transducer are to R, G, B spectral response inconsistency parameter;
Then, whole compensating images is divided, be divided into a plurality of little image blocks, therefrom choose a little image block or a plurality of little image block, determine the fiducial value of gray value according to this reference area as reference area.
When choosing reference area, the mean value of the gray value of all little image blocks of at first calculate dividing and the variance of gray value, the variance of the gray value of each little image block and the variance threshold values of setting are compared, when the variance of the gray value of certain little image block during less than the variance threshold values set, then think, this little image block is exactly a reference area, the mean value of this little image block corresponding gray is exactly the fiducial value of gray value, when reference area has when a plurality of, then that the mean value of the gray value of a plurality of little image blocks of these a plurality of reference area correspondences is average again, with this again mean value as the fiducial value of gray value.
Wherein, original image to be detected comprises the Bayer image.
With above-mentioned two kinds of compensation methodes a kind of image detecting method of the embodiment of the invention is described below, as shown in Figure 2:
Step 200: beginning;
Step 201: several original images to be detected are superposeed;
Step 202: ask for the correcting image transducer to R, G, the corresponding inconsistent parameter of B spectrum;
Step 203: ask for offset lens image non-uniformity parameter;
Step 204: according to the parameter that step 202 and step 203 obtain, the image that several original images stacks to be detected that step 201 is obtained obtain compensates the image after being compensated;
Step 205: the original image after the whole compensation is divided into a plurality of little image blocks, asks the mean value of gray value of each little image block and the variance yields of gray value;
Step 206: the variance yields of the gray value that step 205 is tried to achieve and the variance threshold values of setting compare;
Step 207: when the variance yields of the gray value of a certain little image block during less than the variance threshold values set, think that then this piece is reference area, the mean value of the gray scale of this piece correspondence is fiducial value; When a plurality of little image blocks satisfy condition, then average again the mean value of the gray value of a plurality of little image blocks, with this again mean value as the fiducial value of gray value;
Step 208: each gray values of pixel points in the image after the compensation that step 204 is obtained, the fiducial value of the gray value that obtains with step 207 compares, and whether the absolute value of judging both differences is during less than default difference threshold, if, carry out step 209, otherwise, carry out step 210;
Step 209: think that this pixel is a normal point;
Step 210: think that this pixel is a bad point.
The parameter of lens imaging inhomogeneity is repaid in supplement in the embodiment of the invention, has a lot of methods to realize in the prior art, provides following implementation method in the embodiment of the invention, as shown in Figure 3;
Step 301: according to the parameter of the feature extraction lens of lens;
Step 302:, determine correction parameter according to the lens parameter that step 301 obtains;
Step 303: obtain the original image that lens become;
Step 304: the original image that the correction parameter that obtains according to step 302 obtains step 303 is operated, and obtains correcting image.
Wherein, lens parameter in the step 301 comprises: lens add acting factor and lens are taken advantage of acting factor, wherein, the processing of extracting lens parameter comprises: the output image that the input picture of lens obtains when being zero is a picture black, determine that lens add acting factor and equate with picture black, it is white image that the input picture of lens obtains output image when being even brightness blank sheet of paper or grey paper, determines that it is the difference of white image and picture black and the merchant of a constant that lens are taken advantage of acting factor.This constant is the maximum in the white image and the difference of the minimum value in the picture black.
Correction parameter comprises: correction adds acting factor and acting factor is taken advantage of in correction, the processing of determining correction parameter according to the gained lens parameter comprises: lens are added acting factor get negative and obtain proofreading and correct and add acting factor, take advantage of acting factor to get inverse to lens and obtain proofreading and correct and take advantage of acting factor.Correction parameter operated original image pass through formula
R
e(x，y)＝[Out(x，y)+Reoffset(x，y)]×[Regain(x，y)]
Wherein, (x y) is original image, R to Out
e(x y) is correcting image, and (x y) adds acting factor for proofreading and correct to Reoffset, and (x y) takes advantage of acting factor for proofreading and correct to Regain.
Ask correcting image to R in the embodiment of the invention, G, the inconsistent parameter of B spectral response has a lot of implementation methods in the prior art, and the invention process provides following method explanation.
Shooting one uniform hawk or blank (such as, Green reaches. the blank of Macbeth (Gretagmacbeth) company (white balance card)) and, obtain piece image.Ask for the R of this whole sub-picture, G, the mean value of B gray value is designated as Rmean respectively, Gmean, Bmean asks for R according to the mean value of trying to achieve, the inconsistency factor of B and G: Rgain, BGain;
RGain＝Gmean/Rmean；
BGain＝Gmean/Bmean；
With the following method can correcting image R, G, B spectral response inconsistent:
Rout(row，col)＝Rin(row，col)*RGain；
Gout(row，col)＝Gin(row，col)；
Bout(row，col)＝Bin(row，col)*BGain；
Wherein, the row-coordinate of row presentation video, the row coordinate of col presentation video.Rin, Gin, Bin represent that the R that imports, G, B gray value, Rout, Gout, Bout represent the R that exports, G, B gray value.
After the superimposed image that adopts said method that several original image stacks to be detected are obtained carried out uniformity compensation, it was inconsistent to have eliminated lens imaging rgb light spectrum response inhomogeneous and imageing sensor.Compensated lens imaging inhomogeneous after, the gray value between the color component of the same race of the image after the whole compensation all is the same; Compensated the rgb light spectrum response of imageing sensor inconsistent after, the gray value between each color component of the image after the whole compensation is the same.Therefore, on the image after the compensation, need not distinguish each color component, can be a gray level image with the image processing after the whole compensation, adopts this method to simplify follow-up processing.
Therefore, if entire image bad point not then obtains a uniform gray level image after overcompensation, if bad point is arranged in the image, then remove bad point, other pixels are uniform gray level image.
The image division that obtains after the compensation is become a plurality of little image blocks, as shown in Figure 4.It can be average division that compensating images is divided, and also can right and wrong on average divide.To be that manually to choose also can be to choose automatically when carrying out the choosing of reference area.Figure 5 shows that the reference area of choosing.
The badly coordinate record of point that detects is got off or stores, judge the standard of the quality of imageing sensor according to how many conducts of bad number.
The embodiment of the invention provides a kind of image detection device, as shown in Figure 6, comprising:
Gather laminating module 610, be used to gather several original images to be detected, and described several original images to be detected are superposeed;
Compensating module 620 is used for superimposed image is carried out uniformity compensation;
Choose module 630, be used for choosing at least one little image block that satisfies condition as reference area from a plurality of little image block of described division;
Described collection laminating module 610 comprises, collecting unit 611 and superpositing unit 612.Wherein, collecting unit 611 is used to gather several original images to be detected; Superpositing unit 612, the mean value of described gray value is asked in the gray value addition that is used for several original image corresponding pixel points to be detected that will gather, and described mean value as each gray values of pixel points, is obtained superimposed image.
Above-mentioned original image to be detected comprises Bel Bayer original image.
Described compensating module 620 comprises, first compensating unit 621 and second compensating unit 622.Wherein, first compensating unit 621 is used for compensating images lens imaging inhomogeneity parameter; Second compensating unit 622 is used for the correcting image transducer to R, G, the inconsistent parameter of B spectral response.
The described module 630 of choosing comprises, computing unit 633, comparing unit 634 and determining unit 635.Wherein, computing unit 633 is used to calculate the mean value of gray value of each little image block of division and the variance yields of gray value; Comparing unit 634 is used for the variance yields of the gray value that will calculate and the variance threshold values of setting and compares; Determining unit 635 is used for determining reference area according to comparing unit 634 result relatively.
Described determining unit 635 comprises, statistics subelement 638 and definite subelement 639.Wherein, statistics subelement 638 is used to add up the little image block of the variance yields of all gray values less than the variance threshold values of setting; Determine subelement 639, all little image blocks that are used for adding up are as reference area.
Described detection module 640 comprises, computing module 641, comparison module 642.Wherein, computing module 641 is used to calculate the mean value of the gray value of described reference area; Comparing unit 642 is used for each gray values of pixel points of described compensation back image and the difference and the difference threshold of described mean value are compared, and detects pixel.
Described comparison module 642 comprises, computing unit 647, comparing unit 646 and detecting unit 643.Wherein, computing unit 647 is used to calculate the difference of each gray values of pixel points of the mean value of gray value of described reference area and the image after the described compensation; Comparing unit 646 is used for the absolute value of more described difference and the difference threshold of setting; Detecting unit 643 is used for detecting pixel according to described comparing unit 646 result relatively.
Described detecting unit 643 comprises, first detecting unit 644 and second detecting unit 645.Wherein, first detecting unit 644 when the absolute value of difference during less than the difference threshold set, determines that then the pixel of this difference correspondence is a normal point; Second detecting unit 645 when the absolute value of difference during greater than the difference threshold set, determines that then the pixel of this difference correspondence is a bad point.
The embodiment of the invention is when carrying out the dead pixel points of images detection, and the viewpoint from the overall situation on the basis of original image detects bad point, and several original images are superposeed, and the image after the stack is compensated, and has improved the signal to noise ratio and the stability of image.Therefore, the precision that adopts the embodiment of the invention can improve dead pixel points of images to detect, reduce amount of calculation.
Obviously, those skilled in the art can carry out various changes and modification to the present invention and not break away from the spirit and scope of the present invention.Like this, if of the present invention these are revised and modification belongs within the scope of claim of the present invention and equivalent technologies thereof, then the present invention also is intended to comprise these changes and modification interior.
Claims (14)
1. an image detecting method is characterized in that, this method may further comprise the steps:
Gather several original images to be detected,, ask the mean value of this additive value,, obtain superimposed image the gray value of this mean value as corresponding pixel points with the gray value addition of described several original image corresponding pixel points to be detected;
Superimposed image is divided, be divided into a plurality of little image blocks, calculate the mean value of gray value of described a plurality of little image blocks and the variance yields of gray value, the variance yields of described gray value and the variance threshold values of setting are compared, when the variance yields of the gray value of little image block during less than the variance threshold values set, with described little image block as reference area;
Ask the mean value of the gray value of described reference area, each gray values of pixel points of compensation back image and the difference and the difference threshold of described mean value are compared;
When the absolute value of the gray values of pixel points of compensation back image and the difference of described mean value during, judge that this pixel is a normal point less than the difference threshold of setting;
When the absolute value of the gray values of pixel points of compensation back image and the difference of described mean value during, judge that this pixel is a bad point greater than the difference threshold of setting.
2. the method for claim 1 is characterized in that, and is further comprising the steps of before the described step that superimposed image is divided:
Superimposed image is carried out uniformity compensation.
3. method as claimed in claim 2 is characterized in that, described superimposed image is carried out uniformity compensation, comprising:
Compensation superimposed image lens imaging inhomogeneity parameter and correcting image transducer are to R, G, the inconsistent parameter of B spectral response.
4. the method for claim 1 is characterized in that, described original image to be detected comprises: Bel Bayer original image.
5. an image detection device is characterized in that, this device comprises:
Gather laminating module, be used to gather several original images to be detected,, ask the mean value of this additive value,, obtain superimposed image the gray value of this mean value as corresponding pixel points with the gray value addition of described several original image corresponding pixel points to be detected;
Divide module, be used for described superimposed image is divided, be divided into a plurality of little image blocks;
Choose module, be used to calculate the mean value of gray value of described a plurality of little image blocks and the variance yields of gray value, the variance yields of described gray value and the variance threshold values of setting are compared, when the variance yields of the gray value of little image block during less than the variance threshold values set, with described little image block as reference area;
Detection module, ask the mean value of the gray value of described reference area with hand, each gray values of pixel points of compensation back image and the difference and the difference threshold of described mean value are compared, when the absolute value of the gray values of pixel points of compensation back image and the difference of described mean value during less than the difference threshold of setting, judge that this pixel is a normal point, when the absolute value of the gray values of pixel points of compensation back image and the difference of described mean value during, judge that this pixel is a bad point greater than the difference threshold of setting.
6. device as claimed in claim 5 is characterized in that, described device also comprises:
Compensating module is used for described superimposed image is carried out uniformity compensation.
7. device as claimed in claim 6 is characterized in that, described compensating module comprises:
First compensating unit is used for compensating images lens imaging inhomogeneity parameter;
Second compensating unit is used for the correcting image transducer to R, G, the inconsistent parameter of B spectral response.
8. device as claimed in claim 5 is characterized in that, described detection module comprises:
Computing module is used to calculate the mean value of the gray value of described reference area;
Comparison module, be used for each gray values of pixel points of described compensation back image and the difference and the difference threshold of described mean value are compared, when the absolute value of the gray values of pixel points of compensation back image and the difference of described mean value during less than the difference threshold of setting, judge that this pixel is a normal point, when the absolute value of the gray values of pixel points of compensation back image and the difference of described mean value during, judge that this pixel is a bad point greater than the difference threshold of setting.
9. device as claimed in claim 8 is characterized in that, described comparison module comprises:
Computing unit is used to calculate the difference of each gray values of pixel points of the mean value of gray value of described reference area and the image after the described compensation;
Comparing unit is used for the absolute value of more described difference and the difference threshold of setting;
Detecting unit is used for detecting pixel according to the result of described comparing unit comparison.
10. device as claimed in claim 9 is characterized in that, described detecting unit comprises:
First detecting unit when the absolute value of difference during less than the difference threshold set, determines that then the pixel of this difference correspondence is a normal point;
Second detecting unit when the absolute value of difference during greater than the difference threshold set, determines that then the pixel of this difference correspondence is a bad point.
11. device as claimed in claim 5 is characterized in that, the described module of choosing comprises:
Computing unit is used to calculate the mean value of gray value of each little image block of described division and the variance yields of gray value;
Comparing unit is used for the variance yields of described gray value and the variance threshold values of setting are compared;
Determining unit is used for according to described comparing unit result relatively, when the variance yields of the gray value of little image block during less than the variance threshold values set, with described little image block as definite reference area.
12. device as claimed in claim 11 is characterized in that, described determining unit comprises:
The statistics subelement is used to add up the little image block of the variance yields of all gray values less than the variance threshold values of setting;
Determine subelement, be used for all little image blocks with described statistics as reference area.
13. device as claimed in claim 5 is characterized in that, described collection laminating module comprises:
Collecting unit is used to gather several original images to be detected;
Superpositing unit is used for the gray value addition of several original image corresponding pixel points to be detected of described collection is asked the mean value of described gray value, and described mean value as each gray values of pixel points, is obtained superimposed image.
14. device as claimed in claim 5 is characterized in that, described original image to be detected comprises:
Bel Bayer original image.
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| CN101715050B (en) * | 2009-11-20 | 2014-07-09 | 北京中星微电子有限公司 | Method and system for detecting dead pixels of image sensor |
| CN102496006B (en) * | 2011-12-05 | 2013-07-03 | 北方工业大学 | Blank paper detection method |
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| CN104036471B (en) * | 2013-03-05 | 2017-07-25 | 腾讯科技（深圳）有限公司 | A kind of picture noise estimation method and picture noise valuation device |
| CN104363391B (en) * | 2014-11-28 | 2018-11-27 | 广东欧珀移动通信有限公司 | Dead pixel points of images compensation method, system and photographing device |
| CN104935838B (en) * | 2015-06-04 | 2018-04-06 | 上海集成电路研发中心有限公司 | A kind of method of image restoring |
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| CN108426640B (en) * | 2018-02-28 | 2019-05-10 | 北京理工大学 | A kind of bearing calibration for infrared detector defect pixel |
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| CN109959498A (en) * | 2019-03-29 | 2019-07-02 | 珠海达明科技有限公司 | A kind of array light-emitting component quality detecting system and its application method |
| CN110288599B (en) * | 2019-07-10 | 2021-06-15 | 浙江大华技术股份有限公司 | Dead pixel detection method and device, electronic equipment and storage medium |
| CN111031307A (en) * | 2019-12-20 | 2020-04-17 | 昆山丘钛微电子科技有限公司 | Camera module detection method and device, electronic equipment and medium |
| CN113873229B (en) * | 2021-09-26 | 2024-02-27 | 江西盛泰精密光学有限公司 | Image dead pixel detection method, system and device |
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