CN1885344A - High-resolution detection method for image gray scale/chromaticity information for base image mining - Google Patents
High-resolution detection method for image gray scale/chromaticity information for base image mining Download PDFInfo
- Publication number
- CN1885344A CN1885344A CN 200610054324 CN200610054324A CN1885344A CN 1885344 A CN1885344 A CN 1885344A CN 200610054324 CN200610054324 CN 200610054324 CN 200610054324 A CN200610054324 A CN 200610054324A CN 1885344 A CN1885344 A CN 1885344A
- Authority
- CN
- China
- Prior art keywords
- gray
- image
- level
- original image
- gray level
- 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.)
- Granted
Links
Images
Landscapes
- Image Analysis (AREA)
Abstract
The invention relates to a high-revolution checking method of image gray/color information used in bottom image development, which comprises: 1, obtaining the gray/color value of each pixel; 2, calculating the pixel number of each gray/color level; 3, building gray/color spectrum of original image; 4, calculating the total pixel number of original image; 5, classifying and flattening the gray/color spectrum of original image, using the pixel number as longitudinal coordinate, and gray/color level as transverse coordinate to be the gray/color spectrum that classified and flattened; 6, selecting right flattened level, to obtain relative flattened gray/color spectrum to combine original image gray/color spectrum, to check if the original image has hidden bottom image information. The invention has the revolution at one gray/color level, to find the image information hidden by the strong background, which can not be seen by people.
Description
Technical field
The invention belongs to Flame Image Process and transmission technique field, specifically, relate to a kind of in the bottom layer image mining process, the method that the image information that the strong background of quilt that can not see people's vision has been buried detects.
Background technology
Human gray limiting resolution is about the difference of 4～5 levels, and the human eyesight of image information that is lower than this limit can't be differentiated at all, therefore can utilize the gray resolution restriction of human vision to realize hiding of target image; Correspondingly, the target image of hiding or buried by strong background is excavated in order to obtain target image.This method can be used for text, image encrypting and decrypting, the transmission of maintaining secrecy, or excavate captured image information under the mal-condition is as the image information of escaping behind the automobile accident, hiding in financial institution's monitoring image etc., these information can be automobile profile, the trade mark or textile design, or even dna image.In the conventional images treatment technology, when excavating the bottom layer image information that the human vision of being buried by strong background can not see, what adopt usually is the gray-level histogram equalization method, its specific practice is: 256 gray levels of image 0 to 255 are divided into plurality of sections, as 16 sections or 32 sections, obtain respectively that gray scale belongs to certain section pixel count in the piece image, make histogram (as shown in Figure 2) then, to observe the statistical distribution of gray scale.The used formula of the algorithm of histogram equalization is TE
i=p/q, in the formula subscript i represent segments (i=1,2 ..., q), P is the total pixel number of piece image, TE
iBe the pixel count of i section, promptly the pixel count of each gray level is identical in every section, so be called the equalization histogram.This equalization histogram does not contain important information, and resolution is low, the bottom layer image less than 4～5 gray level differences cannot be detected and excavate, so its application is very limited.
Summary of the invention
The purpose of this invention is to provide a kind of gray class resolution ratio that has, be used for the high-resolution detection method of the gradation of image/chrominance information of bottom layer image mining, this method can detect the image information that the strong background of quilt that human vision can not see has been buried.
For achieving the above object, the high-resolution detection method that is used for the gradation of image/chrominance information of bottom layer image mining of the present invention, undertaken by following step:
(1) image is carried out gray conversion, the gray value of each pixel in the computed image;
(2) after the gray value of each pixel, add up the pixel count of each gray level respectively in obtaining image, described colourity is meant the colourity of every kind of color in three kinds of colors of red, green, blue;
(3) pixel count with each gray level is an ordinate, and the gray level is the gray that horizontal ordinate is made original image respectively;
(4) calculate the total pixel count of original image;
(5) gray of original image is carried out gradually flattening, and be ordinate with the pixel count of each gray level, the gray level is the gray after horizontal ordinate is made gradually flattening respectively, and used formula is:
TE
i＝(OZ
i)
1/m∑TE
i/∑(OZ
i)
1/m
I=0 in the formula, 1,2 ..., N-1, N=256, expression gray level, OZ
iBe the pixel count of i gray level in the original image, TE
iBe the pixel count of i gray level behind the gradually flattening, [O ∞) is the planarization level to m ∈, according to image total pixel number invariance principle before and after the conversion, ∑ TE in the above-mentioned formula
i=∑ OZ
i
(6) select suitable planarization level, obtain gray after the planarization of this grade correspondence, and, just can detect whether hiding bottom layer image information is arranged in the original image in conjunction with the original image gray.
In the present invention, calculate in the piece image pixel count of each gray level in 0～255 gray level respectively, be that ordinate (adopts the normalization mapping with the pixel count that belongs to each gray then, its value is shared percentage), the gray level is that horizontal ordinate is made discrete X-Y scheme, this X-Y scheme is similar to the spectral line in the power spectrum, so be called gray.It is a gray histogram that gray is differential that this gray is equivalent to resolution, has a high resolving power that gray is differential.We know that when a real number greater than 1 was opened the m power, m is big more, and the value that draws was more little, and the value that m draws when being tending towards infinite is 1; And when an arithmetic number less than 1 opened the m power, the big more value that draws of m was also big more, and the value that m draws when being tending towards infinite also is 1.According to this principle, we are using formula TE
i=∑ TE
i(OZ
i)
1/m/ ∑ (OZ
i)
1/mObtain the gray behind the gradually flattening, original gray before the combining image gradually flattening judges whether have hiding image information in the original image, concrete reckoning process is: when m=1, the gray that obtains according to above-mentioned formula is exactly the gray of original image; M＞1 o'clock, pixel number in the original gray on each gray level is redistributed, the more gray level of pixel distributes one part of pixel to select on the less gray level of original image vegetarian refreshments on the original gray, increase along with m, it is equal that the pixel number that distributes on each gray level is tending towards more, it is emphasized that no matter what value m gets on the gray level that does not have pixel to exist in the original gray, does not have pixel number in the collection of illustrative plates behind the gradually flattening and exists.When m gets suitable value, the spectral line that contains a few pixels dot information just can come out in the gradually flattening collection of illustrative plates saliency of this grade correspondence, just can judge in conjunction with original gray and whether to have the human vision that is subjected to that has been hidden in the original image and differentiate the gray information of the image that restriction can not find, or the gray information of the image that flooded of the strong background of the quilt that under particular surroundings, obtains, or the gray information that only has a few pixels to occupy in the image.Thereby in bottom layer image mining, just can judge whether be concealed with the target image information that the strong background of quilt that human vision can not see has been buried in the source images according to the distribution situation of pixel in certain grade of planarization gray that obtains, be to excavate image information, particularly excavate the strong instrument of the image information of having hidden or under special mal-condition, obtain.
Remarkable result of the present invention is: have the high resolving power of an ash/gamut of chromaticities, the image information that has been hidden that can excavate that human vision can not see; Or the image information of having been buried by strong background; Or the gray information that a few pixels is occupied in the image.It is the important tool of finding and detect bottom layer image information.This method can be used for the encryption and decryption of text, image, the high density transmission, or excavate captured image information under mal-condition, as the image information of hiding in escape, the financial institution's monitoring image behind the automobile accident.These image informations can be automobile profiles, the trade mark, textile design, or even dna image.
Description of drawings
Fig. 1 is the process flow diagram of the inventive method;
Fig. 2 is a histogrammic synoptic diagram in traditional histogram equalization method;
Fig. 3 (a), 3 (b), 3 (c) are respectively the gray scale spectrogram of original image among the embodiment 1,1 grade of planarization gray scale spectrogram and 4 grades of planarization gray scale spectrograms;
Fig. 4 (a), 4 (b), 4 (c) are respectively the gray scale spectrogram of original image among the embodiment 2,1 grade of planarization gray scale spectrogram and 4 grades of planarization gray scale spectrograms.
Embodiment
Below in conjunction with the drawings and specific embodiments the present invention is described in further detail.
Embodiment 1: suppose that source images is is 0 black image in a slice gray level, wherein being concealed with one is the pentagram that 2 black lines is drawn as with gray level, human vision can not tell whether be concealed with pentagram in this black image, can not detect according to traditional equalization histogram method.As shown in Figure 1, adopt the detection step of the inventive method to be:
(1) the source figure image file with specified format reads in computing machine, or read in computing machine after image file being converted to the form of appointment, carry out the gradation conversion of image then by computer program, obtain the gray-scale value of each pixel in the image, its conversion formula can be with normalization weighted sum formula, also can weigh conversion formula with waiting, wherein:
Normalization weighted sum formula is: the blue chromatic value of gray-scale value=red chromatic value * 0.3+ greenness value * 0.59+ * 0.11;
Deng the power conversion formula be: the blue chromatic value of gray-scale value=red chromatic value/3+ greenness value/3+/3.
(2) in obtaining image, behind the gray-scale value of each pixel, count the pixel count of each gray level in the gray scale respectively.
(3) pixel count with each gray level after the normalization is an ordinate, and gray level is the gray scale spectrum that horizontal ordinate is made original image, shown in Fig. 3 (a).
(4) calculate the total pixel count of original image.
(5) gray scale of original image spectrum is carried out gradually flattening, and be ordinate with the pixel count of each gray level after the normalization, gray level is the gray scale spectrum after horizontal ordinate is made gradually flattening, and used formula is:
TE
i＝(OZ
i)
1/m∑TE
i/∑(OZ
i)
1/m
I=0 in the formula, 1,2 ..., N-1, N=256, expression gray level, OZ
iBe the pixel count of i gray level in the original image, TE
iBe the pixel count of i gray level behind the gradually flattening, and m ∈ [0, ∞) be the planarization level, according to image total pixel number invariance principle before and after the conversion, ∑ TE in the above-mentioned formula
i=∑ OZ
i
(6) select suitable planarization level, obtain gray scale spectrum after the planarization of this grade correspondence, and, just can detect whether hiding bottom layer image information is arranged in the original image in conjunction with original image gray scale spectrum.Shown in Fig. 3 (b), when the planarization level was 1 (m=1 in the formula), the 1 grade of planarization gray scale spectrogram that obtains was identical with the gray scale spectrum of the original image shown in Fig. 3 (a), can only find that gray level is 0 image information, does not see the image information of wherein hiding; When the planarization level is 4 (m=4 in the formula), 4 grades of planarization gray scale spectrograms that obtain are shown in Fig. 3 (c), 2 grades of gray scale compositions are still arranged except 0 grade of gray scale composition as can be seen, can judge in view of the above to be concealed with other image information in the black background image that it is pentagram that utilization bottom layer image mining method just can further be excavated bottom layer image.
Embodiment 2: suppose that source images is a zone that gray scale is 255 (whites), having a gray scale therein is 252 pentagram, and clearly seeming whole is a slice white, and people's vision is not seen any picture structure.According to flow process shown in Figure 1, adopt the detection step of the inventive method to be:
(1) the source figure image file with specified format reads in computing machine, or read in computing machine after image file being converted to the form of appointment, carry out the gradation conversion of image then by computer program, obtain the gray-scale value of each pixel in the image, its conversion formula can be with normalization weighted sum formula, also can weigh conversion formula with waiting, wherein:
Normalization weighted sum formula is: the blue chromatic value of gray-scale value=red chromatic value * 0.3+ greenness value * 0.59+ * 0.11;
Deng the power conversion formula be: the blue chromatic value of gray-scale value=red chromatic value/3+ greenness value/3+/3.
(2) in obtaining image, behind the gray-scale value of each pixel, count the pixel count of each gray level in the gray scale respectively.
(3) pixel count with each gray level after the normalization is an ordinate, and gray level is the gray scale spectrum that horizontal ordinate is made original image, shown in Fig. 4 (a).
(4) calculate the total pixel count of original image.
(5) gray scale of original image spectrum is carried out gradually flattening, and be ordinate with the pixel count of each gray level after the normalization, gray level is the gray scale spectrum after horizontal ordinate is made gradually flattening, and used formula is:
TE
i＝(OZ
i)
1/m∑TE
i/∑(OZ
i)
1/m
I=0 in the formula, 1,2 ..., N-1, N=256, expression gray level, 0Z
iBe the pixel count of i gray level in the original image, TE
iBe the pixel count of i gray level behind the gradually flattening, and m ∈ [0, ∞) be the planarization level, according to image total pixel number invariance principle before and after the conversion, ∑ TE in the above-mentioned formula
i=∑ OZ
i
(6) select suitable planarization level, obtain gray scale spectrum after the planarization of this grade correspondence, and, just can detect whether hiding bottom layer image information is arranged in the original image in conjunction with original image gray scale spectrum.Shown in Fig. 4 (b), when the planarization level was 1 (m=1 in the formula), the 1 grade of planarization gray scale spectrogram that obtains was identical with the gray scale spectrum of the original image shown in Fig. 4 (a), can only find that gray level is 255 image information, does not see the image information of wherein hiding; When the planarization level is 4 (m=4 in the formula), 4 grades of planarization gray scale spectrograms that obtain are shown in Fig. 4 (c), 252 grades of gray scale compositions are still arranged except 255 grades of gray scale compositions as can be seen, can judge in view of the above to be concealed with other image information in the white background picture that it is pentagram that utilization bottom layer image mining method just can further be excavated bottom layer image.
If bottom layer image is a coloured image, can detect the chrominance information of bottom layer image according to the inventive method in the same old way high-resolution, thereby be follow-up excacation provider to.
In above-mentioned two embodiment, when the value of planarization level m gets 4, the gray scale spectral line of hidden image has just shown especially, principle according to the inventive method, if the value of m increases gradually, the spectral line of bottom layer image corresponding grey scale level increases gradually, and when m was tending towards infinity, two height of spectral line trends were approaching.
Claims (1)
1, a kind of high-resolution detection method that is used for the gradation of image/chrominance information of bottom layer image mining is characterized in that comprising the following steps:
(1) image is carried out gray conversion, the gray value of each pixel in the computed image;
(2) after the gray value of each pixel, add up the pixel count of each gray level respectively in obtaining image, described colourity is meant the colourity of every kind of color in three kinds of colors of red, green, blue;
(3) pixel count with each gray level is an ordinate, and the gray level is the gray that horizontal ordinate is made original image respectively;
(4) calculate the total pixel count of original image;
(5) gray of original image is carried out gradually flattening, and be ordinate with the pixel count of each gray level, the gray level is the gray after horizontal ordinate is made gradually flattening respectively, and used formula is:
TE
i＝(OZ
i)
1/m∑TE
i/∑(OZ
i)
1/m
I=0 in the formula, 1,2 ..., N-1, N=256, expression gray level, OZ
iBe the pixel count of i gray level in the original image, TE
iBe the pixel count of i gray level behind the gradually flattening, and m ∈ [0, ∞) be the planarization level, according to image total pixel number invariance principle before and after the conversion, ∑ TE in the above-mentioned formula
i=∑ OZ
i
(6) select suitable planarization level, obtain gray after the planarization of this grade correspondence, and, just can detect whether hiding bottom layer image information is arranged in the original image in conjunction with the original image gray.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2006100543249A CN100383822C (en) | 2006-05-25 | 2006-05-25 | High-resolution detection method for image gray scale/chromaticity information for base image mining |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2006100543249A CN100383822C (en) | 2006-05-25 | 2006-05-25 | High-resolution detection method for image gray scale/chromaticity information for base image mining |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1885344A true CN1885344A (en) | 2006-12-27 |
| CN100383822C CN100383822C (en) | 2008-04-23 |
Family
ID=37583483
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB2006100543249A Expired - Fee Related CN100383822C (en) | 2006-05-25 | 2006-05-25 | High-resolution detection method for image gray scale/chromaticity information for base image mining |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN100383822C (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101419707B (en) * | 2008-11-28 | 2010-12-08 | 重庆医科大学 | Best quality image acquiring method in bottom layer image mining |
| CN101441770B (en) * | 2008-11-28 | 2012-03-21 | 重庆医科大学 | Method for excavating optimum image based on information entropy and logarithm contrast weight sum |
| CN102800060A (en) * | 2012-06-26 | 2012-11-28 | 重庆医科大学 | Quick self-adaption optimizing method for digital image at low illumination level |
| CN102800061A (en) * | 2012-06-26 | 2012-11-28 | 重庆医科大学 | High-illuminance digital image fast adaptive optimization method |
| CN110322521A (en) * | 2019-07-10 | 2019-10-11 | 南充职业技术学院 | The chrominance information method for digging of latent image |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101668226B (en) * | 2009-09-11 | 2011-05-04 | 重庆医科大学 | Method for acquiring color image with best quality |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1194530C (en) * | 2003-05-10 | 2005-03-23 | 合肥工业大学 | Image waterprint method for copyright protection |
| CN1276387C (en) * | 2004-06-10 | 2006-09-20 | 上海交通大学 | Synchronous self-adaptable watermark method based on image continuity |
| CN1317679C (en) * | 2004-11-03 | 2007-05-23 | 上海大学 | Lossless information concealing method based on gray scale adjustment |
| CN1323371C (en) * | 2005-08-15 | 2007-06-27 | 西安交通大学 | Fingerprint information hiding and verifying method based on two-dimensional bar code |
-
2006
- 2006-05-25 CN CNB2006100543249A patent/CN100383822C/en not_active Expired - Fee Related
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101419707B (en) * | 2008-11-28 | 2010-12-08 | 重庆医科大学 | Best quality image acquiring method in bottom layer image mining |
| CN101441770B (en) * | 2008-11-28 | 2012-03-21 | 重庆医科大学 | Method for excavating optimum image based on information entropy and logarithm contrast weight sum |
| CN102800060A (en) * | 2012-06-26 | 2012-11-28 | 重庆医科大学 | Quick self-adaption optimizing method for digital image at low illumination level |
| CN102800061A (en) * | 2012-06-26 | 2012-11-28 | 重庆医科大学 | High-illuminance digital image fast adaptive optimization method |
| CN102800060B (en) * | 2012-06-26 | 2016-05-11 | 重庆医科大学 | The quick self-adapted optimization method of digital picture under low-light (level) |
| CN102800061B (en) * | 2012-06-26 | 2016-05-11 | 重庆医科大学 | The quick self-adapted optimization method of digital picture under high illumination |
| CN110322521A (en) * | 2019-07-10 | 2019-10-11 | 南充职业技术学院 | The chrominance information method for digging of latent image |
| CN110322521B (en) * | 2019-07-10 | 2022-12-23 | 南充职业技术学院 | Method for mining chrominance information of invisible image |
Also Published As
| Publication number | Publication date |
|---|---|
| CN100383822C (en) | 2008-04-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN1885344A (en) | High-resolution detection method for image gray scale/chromaticity information for base image mining | |
| CN1704883A (en) | System and method for displaying images utilizing multi-blending | |
| CN1940695A (en) | Method for compensating a picture | |
| US20100254601A1 (en) | Method and device for use in converting a colour image into a grayscale image | |
| CN101916427B (en) | Spatial domain-based image watermark adding method | |
| CN101795350B (en) | Non-linear image double amplifying method based on relevance detection | |
| CN106875923A (en) | A kind of pixel rendering method and pixel rendering device | |
| CN104021518B (en) | Reversible data hiding method for high dynamic range image | |
| CN104376260A (en) | Malicious code visualized analyzing method based on Shannon information entropy | |
| Juneja et al. | Application of LSB based steganographic technique for 8-bit color images | |
| CN116137022B (en) | Data enhancement method for underground mining remote monitoring | |
| EP1450337A3 (en) | Display panel driver having multi-grayscale processing function | |
| CN100377169C (en) | Method for picture binaryzation | |
| CN105787965B (en) | A kind of image search method based on color characteristic | |
| CN107742508A (en) | Driving method and driving device of display device | |
| CN100527164C (en) | Mining method for low-level image and image mining apparatus employing the same | |
| CN1386246A (en) | Window detection | |
| CN100392675C (en) | Method for hiding and excavating bottom image and device thereby | |
| CN1297938C (en) | Method and apparatus for inserting and detecting watermark in picture derived from source picture | |
| CN110689860A (en) | Image representation method, device and computer storage medium | |
| EP3296957A1 (en) | Method and apparatus for judging image brightness background, and display apparatus | |
| CN1725868A (en) | Method for detecting GIF infomration hidden | |
| Tilakaratne et al. | Image steganography scheme based on reversible data embedding strategy | |
| Zhan et al. | Three-dimensional prediction-error histograms based reversible data hiding algorithm for color images | |
| US20070177171A1 (en) | Inking on photographs |
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 | ||
| C17 | Cessation of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20080423 Termination date: 20110525 |