CN110322521A - The chrominance information method for digging of latent image - Google Patents
The chrominance information method for digging of latent image Download PDFInfo
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Abstract
The invention discloses a kind of chrominance information method for digging of latent image, comprising: obtains original image；Original image is obtained respectively in the channel image of each image channel；Calculate separately the dot frequency of each gamut of chromaticities of each channel image and effective gamut of chromaticities in each channel；Calculate separately the target colorimetric grade value of each gamut of chromaticities of each channel image；The target colorimetric grade value of each gamut of chromaticities of each channel image is normalized respectively；The target colorimetric grade value and original colorimetric grade value being respectively compared in each channel image after the normalization of each gamut of chromaticities, obtain the chrominance information of latent image.No matter the solution of the present invention excavates series is how many, pixel in original image there is no gamut of chromaticities always will not be with the presence of gamut of chromaticities, originally there are the chromatic values of the pixel of gamut of chromaticities to change, and can more accurately excavate the latent image information in image.
Description
Technical field
The invention belongs to processing technology fields, more particularly to a kind of chrominance information method for digging of latent image.
Background technique
Under nature adverse circumstances (for example, ambient light is according to insufficient, illumination is too strong, has the environment such as mist, underwater), acquisition
Digital picture be easy to produce it is dark, excessively bright, have mist, have phenomena such as water, occur the unrecognized images letter of many naked eyes in image
Breath, referred to as Invisible Image.For improving image quality, it will usually image enhancement recovery is carried out, and it is multiple carrying out image enhancement
It needs to analyze existing image information before former.
The image information analysis tool of extensive utilization is gray scale/color histogram at present.Grey level histogram is about gray scale
The function of grade distribution is the statistics to gray level in image [0,255] totally 256 grades of intensity profile.Grey level histogram is will be digital
All pixels in image count the frequency of its appearance according to the size of gray value.Color histogram refers in certain color sky
Between in (such as RGB color), color level in some Color Channel (channel R, the channel G, channel B, gray channel) [0,
255] ratio shared by.Here the classification in gray scale and each Color Channel is referred to as gamut of chromaticities.
The horizontal axis of general picture represents gamut of chromaticities, the longitudinal axis represents the dot frequency (i.e. pixel number size) on the gamut of chromaticities.
The image acquired in the presence of a harsh environment, due to the probability very little that certain gamut of chromaticities occur, be easy to be submerged in dark illumination, bright illumination,
Become stealthy pixel in mist, water.This pixel is not present, but produces stealth, this picture to the vision system of the mankind
Element is often exactly the important goal information for carrying out image enhancement and recovery.
Histogram is static at present, and stealthy chrominance information can not be expressed intuitively, and the equalization of histogram passes through people
For to each gamut of chromaticities, with the coloration average value of entire image, pixel also occurs in the gray level that will lead to originally not pixel, disobey
Pixel invariance principle has been carried on the back, image pixel structures are artificially changed, follow-up study structure has been may cause and exception occurs.Therefore, it is necessary to
A kind of general, basic, simple accurate method for digging, reappears stealthy pixel, so as to the analysis of information.
Summary of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of chrominance information excavation sides of latent image
Method.
The purpose of the present invention is achieved through the following technical solutions: the chrominance information method for digging of latent image, packet
It includes:
Obtain original image；
Original image is obtained respectively in the channel image of each image channel；
Calculate separately the dot frequency of each gamut of chromaticities of each channel image and effective gamut of chromaticities in each channel；
Calculate separately the target colorimetric grade value of each gamut of chromaticities of each channel image, the calculation formula of target colorimetric grade value
It is as follows:
In formula, ch indicates channel image；I indicates gamut of chromaticities series, and value range is [0,255]；TL(i)chIndicate logical
The target colorimetric grade value of the i-th gamut of chromaticities of road image；OL(i)chIndicate the original colorimetric grade value of the i-th gamut of chromaticities of channel image；T is indicated
The total chromatic value number of channel image；Effective gamut of chromaticities series of N expression channel image；N indicates that equal difference excavates series；K expression etc.
Difference excavates limiting value, (n-1)≤K；
The target colorimetric grade value of each gamut of chromaticities of each channel image is normalized respectively, normalized public affairs
Formula is as follows:
In formula, ch indicates channel image；I indicates gamut of chromaticities series, and value range is [0,255]；TL(i)chIndicate logical
The target colorimetric grade value of the i-th gamut of chromaticities of road image；Indicate the dot frequency value of the maximum chrominance grade of channel image；Indicate the dot frequency value of the minimal color grade of channel image；NL(i)chIndicate the target of the i-th gamut of chromaticities of channel image
Value after the normalization of gamut of chromaticities value；
It is respectively compared target colorimetric grade value and original colorimetric grade in each channel image after the normalization of each gamut of chromaticities
Value, obtains the chrominance information of latent image.
Preferably, the channel image includes gray channel image, R channel image, G channel image and channel B image, this
When target colorimetric grade value calculation formula and normalized formula in, ch ∈ [0,1,2,3] respectively indicates gray channel image, R
Channel image, G channel image and channel B image.
Preferably, the channel includes gray channel image, R channel image, G channel image, channel B image, dark
Image and bright channel image, at this time in the calculation formula of target colorimetric grade value and normalized formula, ch ∈ [0,1,2,3,4,
5], gray channel image, R channel image, G channel image, channel B image, dark channel image and bright channel image are respectively indicated.
Preferably, the acquisition methods of the dark channel image are as follows: calculate each pixel of original image and correspond to the channel R figure
Minimal color value in picture, G channel image and channel B image, deposit corresponding pixel points identical with original image size position
In image.
Preferably, the calculation formula of the object pixel point value of the dark channel image is as follows:
T(x,y)'ch=Min (O (x, y)ch)
In formula, ch ∈ [1,2,3] respectively indicates R channel image, G channel image and channel B image；O(x,y)chIndicate logical
The original pixels point value of road image；T(x,y)'chIndicate the object pixel point value of dark channel image.
Preferably, the acquisition methods of the bright channel image are as follows: calculate each pixel of original image and correspond to the channel R figure
Maximum chrominance value in picture, G channel image and channel B image, deposit corresponding pixel points identical with original image size position
In image.
Preferably, the calculation formula of the object pixel point value of the bright channel image is as follows:
T(x,y)”ch=Max (O (x, y)ch)
In formula, ch ∈ [1,2,3] respectively indicates R channel image, G channel image and channel B image；O(x,y)chIndicate logical
The original pixels point value of road image；T(x,y)"chIndicate the object pixel point value of bright channel image.
Preferably, the chrominance information method for digging of latent image further include:
The original coloration spectrogram of each channel image is generated, and shows that its coloration composes relevant information；
It generates each channel image and obtains the coloration spectrogram after stealthy image information, and show that its coloration composes relevant information.
Preferably, the coloration spectrum relevant information includes total pixel number, average color angle value, maximum chrominance grade, minimal color
Grade and effective spectral line number.
The beneficial effects of the present invention are:
(1) the solution of the present invention can more accurately excavate the letter of the latent image in image compared to traditional histogram
Breath；
(2) the solution of the present invention no matter excavate series be it is how many, in original image there is no gamut of chromaticities pixel always
The chromatic value that with the presence of gamut of chromaticities, will not have originally the pixel of gamut of chromaticities will not change；It will not be as traditional histogram
Equalization, which like that forces each gamut of chromaticities, gives mean pixel number, has but regardless of the gamut of chromaticities in each image channel of original image
No pixel exists；
(3) the solution of the present invention can to gray channel image, R channel image, G channel image, channel B image, help secretly
Road image and bright channel image carry out the accurate excavation of chrominance information, and are intuitively shown in the form of coloration spectrogram.
Detailed description of the invention
Fig. 1 is the flow diagram of an embodiment of the present invention；
Fig. 2 is the flow diagram of another embodiment of the invention；
Fig. 3 is each channel image of low-light (level) image when RGB channel coloration spectrum information excavates；
Fig. 4 is that low-light (level) image RGB channel histogram excavates coloration spectrum when RGB channel coloration spectrum information excavates；
Fig. 5 is that 1 grade of low-light (level) image RGB channel precisely excavates coloration spectrum when RGB channel coloration spectrum information excavates；
Fig. 6 is that 4 grades of low-light (level) image RGB channel precisely excavates coloration spectrum when RGB channel coloration spectrum information excavates；
Fig. 7 is each channel image of high illumination image when RGB channel coloration spectrum information excavates；
Fig. 8 is that high illumination image RGB channel histogram excavates coloration spectrum when RGB channel coloration spectrum information excavates；
Fig. 9 is that 1 grade of high illumination image RGB channel precisely excavates coloration spectrum when RGB channel coloration spectrum information excavates；
Figure 10 is that 4 grades of high illumination image RGB channel precisely excavates coloration spectrum when RGB channel coloration spectrum information excavates；
Figure 11 is each channel image of underwater picture when RGB channel coloration spectrum information excavates；
Figure 12 is that underwater picture RGB channel histogram excavates coloration spectrum when RGB channel coloration spectrum information excavates；
Figure 13 is that 1 grade of underwater picture RGB channel precisely excavates coloration spectrum when RGB channel coloration spectrum information excavates；
Figure 14 is that 4 grades of underwater picture RGB channel precisely excavates coloration spectrum when RGB channel coloration spectrum information excavates；
Figure 15 is each channel image of foggy image when RGB channel coloration spectrum information excavates；
Figure 16 is that foggy image RGB channel histogram excavates coloration spectrum when RGB channel coloration spectrum information excavates；
Figure 17 is that 1 grade of foggy image RGB channel precisely excavates coloration spectrum when RGB channel coloration spectrum information excavates；
Figure 18 is that 4 grades of foggy image RGB channel precisely excavates coloration spectrum when RGB channel coloration spectrum information excavates；
Figure 19 is each channel image of underwater picture when RGB coloration spectrum information in channel dark/bright excavates；
Figure 20 is that underwater picture RGB channel histogram dark/bright excavates coloration when RGB coloration spectrum information in channel dark/bright excavates
Spectrum；
Figure 21 is that underwater picture RGB precisely excavates coloration in 1 grade of channel dark/bright when RGB coloration spectrum information in channel dark/bright excavates
Spectrum；
Figure 22 is that underwater picture RGB precisely excavates coloration in 4 grades of channel dark/bright when RGB coloration spectrum information in channel dark/bright excavates
Spectrum；
Figure 23 is that low-light (level) image conventional histogram equalizes each channel image；
Figure 24 is that 1 grade of low-light (level) image RGB channel precisely excavates coloration spectrum；
Figure 25 is that low-light (level) image conventional histogram equalizes coloration spectrum；
Figure 26 is that 4 grades of low-light (level) image RGB channel precisely excavates coloration spectrum；
Figure 27 is the general spectrum color degree spectrum such as low-light (level) image conventional histogram equalization.
Specific embodiment
Below in conjunction with embodiment, technical solution of the present invention is clearly and completely described, it is clear that described
Embodiment is only a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, this field
Technical staff's every other embodiment obtained under the premise of not making the creative labor belongs to what the present invention protected
Range.
Refering to fig. 1-27, the present invention provides a kind of chrominance information method for digging of latent image:
Embodiment one
As shown in Figure 1, the chrominance information method for digging of latent image, comprising:
S1. original image is obtained.
S2. original image is obtained respectively in the channel image of each image channel.
Step S2 is specifically included: obtaining the gray channel image that original image is shown in gray level image channel；It obtains
The R channel image that original image is shown in R image channel；Original image is obtained in the channel G that G image channel is shown
Image；Obtain the channel B image that original image is shown in B image channel.
S3. the dot frequency of each gamut of chromaticities of each channel image and effective coloration in each channel are calculated separately
Grade.
Step S3 is specifically included:
The dot frequency of each gamut of chromaticities (range of gamut of chromaticities is [0,255]) of gray channel image is calculated, and
Effective gamut of chromaticities (effective gamut of chromaticities: the gamut of chromaticities that dot frequency is not zero) of gray channel image；
Dot frequency and the R for calculating each gamut of chromaticities (range of gamut of chromaticities is [0,255]) of R channel image are logical
Effective gamut of chromaticities (effective gamut of chromaticities: the gamut of chromaticities that dot frequency is not zero) of road image；
Dot frequency and the G for calculating each gamut of chromaticities (range of gamut of chromaticities is [0,255]) of G channel image are logical
Effective gamut of chromaticities (effective gamut of chromaticities: the gamut of chromaticities that dot frequency is not zero) of road image；
Dot frequency and the B for calculating each gamut of chromaticities (range of gamut of chromaticities is [0,255]) of channel B image are logical
Effective gamut of chromaticities (effective gamut of chromaticities: the gamut of chromaticities that dot frequency is not zero) of road image.
S4. the target colorimetric grade value for calculating separately each gamut of chromaticities of each channel image, that is, calculate separately gray channel
Image, R channel image, G channel image and channel B image each gamut of chromaticities target colorimetric grade value.
The calculation formula of target colorimetric grade value is as follows:
In formula, ch ∈ [0,1,2,3] indicates each channel image, specifically, ch ∈ [0] indicates gray channel image, ch ∈
[1] R channel image is indicated, ch ∈ [2] indicates G channel image, and ch ∈ [3] indicates channel B image；I indicates gamut of chromaticities series,
Value range is [0,255]；TL(i)chIndicate the target colorimetric grade value of the i-th gamut of chromaticities of channel image；OL(i)chIndicate channel figure
As the original colorimetric grade value of the i-th gamut of chromaticities；T indicates the total chromatic value number of channel image；Effective gamut of chromaticities of N expression channel image
Series；N indicates that equal difference excavates series；K indicates that equal difference excavates limiting value, (n-1)≤K；The value self-setting of n and K.
S5. the target colorimetric grade value of each gamut of chromaticities of each channel image is normalized respectively, that is, distinguished
The target colorimetric grade value of each gamut of chromaticities of gray channel image, R channel image, G channel image and channel B image is carried out
Normalized.
Normalized formula is as follows:
In formula, ch ∈ [0,1,2,3] indicates each channel image, specifically, ch ∈ [0] indicates gray channel image, ch ∈
[1] R channel image is indicated, ch ∈ [2] indicates G channel image, and ch ∈ [3] indicates channel B image；I indicates gamut of chromaticities series,
Value range is [0,255]；TL(i)chIndicate the target colorimetric grade value of the i-th gamut of chromaticities of channel image；Indicate channel figure
The dot frequency value of the maximum chrominance grade of picture；Indicate the dot frequency value of the minimal color grade of channel image；NL(i)ch
Value after indicating the target colorimetric grade value normalization of the i-th gamut of chromaticities of channel image.
S6. it is respectively compared in each channel image (gray channel image, R channel image, G channel image and channel B image)
Target colorimetric grade value and original colorimetric grade value after the normalization of each gamut of chromaticities, obtain the chrominance information of latent image.
In some embodiments, the chrominance information method for digging of latent image further include:
The original coloration spectrogram of each channel image is generated, and shows that its coloration composes relevant information, the coloration spectrum is related to be believed
Breath includes total pixel number, average color angle value, maximum chrominance grade, minimal color grade and effective spectral line number；
It generates each channel image and obtains the coloration spectrogram after stealthy image information, and show that its coloration composes relevant information,
The coloration spectrum relevant information includes total pixel number, average color angle value, maximum chrominance grade, minimal color grade and effective spectral line number.
Embodiment two
As shown in Fig. 2, the chrominance information method for digging of latent image, comprising:
S1. original image is obtained.
S2. original image is obtained respectively in the channel image of each image channel.
Step S2 is specifically included: obtaining the gray channel image that original image is shown in gray level image channel；It obtains
The R channel image that original image is shown in R image channel；Original image is obtained in the channel G that G image channel is shown
Image；Obtain the channel B image that original image is shown in B image channel；Original image is obtained to show in dark image channel
Obtained dark channel image；Obtain the bright channel image that original image is shown in bright image channel.
The acquisition methods of the dark channel image are as follows: calculate each pixel of original image and correspond to R channel image, the channel G
Minimal color value in image and channel B image is stored in the image of corresponding pixel points identical with original image size position.Institute
The calculation formula for stating the object pixel point value of dark channel image is as follows:
T(x,y)'ch=Min (O (x, y)ch)
In formula, ch ∈ [1,2,3] respectively indicates R channel image, G channel image and channel B image；O(x,y)chIndicate logical
The original pixels point value of road image；T(x,y)'chIndicate the object pixel point value of dark channel image.
The acquisition methods of the bright channel image are as follows: calculate each pixel of original image and correspond to R channel image, the channel G
Maximum chrominance value in image and channel B image is stored in the image of corresponding pixel points identical with original image size position.Institute
The calculation formula for stating the object pixel point value of bright channel image is as follows:
T(x,y)”ch=Max (O (x, y)ch)
In formula, ch ∈ [1,2,3] respectively indicates R channel image, G channel image and channel B image；O(x,y)chIndicate logical
The original pixels point value of road image；T(x,y)"chIndicate the object pixel point value of bright channel image.
S3. the dot frequency of each gamut of chromaticities of each channel image and effective coloration in each channel are calculated separately
Grade.
Step S3 is specifically included:
The dot frequency of each gamut of chromaticities (range of gamut of chromaticities is [0,255]) of gray channel image is calculated, and
Effective gamut of chromaticities (effective gamut of chromaticities: the gamut of chromaticities that dot frequency is not zero) of gray channel image；
Dot frequency and the R for calculating each gamut of chromaticities (range of gamut of chromaticities is [0,255]) of R channel image are logical
Effective gamut of chromaticities (effective gamut of chromaticities: the gamut of chromaticities that dot frequency is not zero) of road image；
Dot frequency and the G for calculating each gamut of chromaticities (range of gamut of chromaticities is [0,255]) of G channel image are logical
Effective gamut of chromaticities (effective gamut of chromaticities: the gamut of chromaticities that dot frequency is not zero) of road image；
Dot frequency and the B for calculating each gamut of chromaticities (range of gamut of chromaticities is [0,255]) of channel B image are logical
Effective gamut of chromaticities (effective gamut of chromaticities: the gamut of chromaticities that dot frequency is not zero) of road image；
The dot frequency of each gamut of chromaticities (range of gamut of chromaticities is [0,255]) of dark channel image is calculated, and dark
Effective gamut of chromaticities (effective gamut of chromaticities: the gamut of chromaticities that dot frequency is not zero) of channel image；
Calculate the dot frequency of each gamut of chromaticities (range of gamut of chromaticities is [0,255]) of bright channel image, Yi Jiliang
Effective gamut of chromaticities (effective gamut of chromaticities: the gamut of chromaticities that dot frequency is not zero) of channel image.
S4. the target colorimetric grade value for calculating separately each gamut of chromaticities of each channel image, that is, calculate separately gray channel
Image, R channel image, G channel image, channel B image, dark channel image and bright channel image each gamut of chromaticities target
Gamut of chromaticities value.
The calculation formula of target colorimetric grade value is as follows:
In formula, ch ∈ [0,1,2,3,4,5] indicates each channel image, specifically, ch ∈ [0] indicates gray channel image,
Ch ∈ [1] indicates R channel image, and ch ∈ [2] indicates G channel image, and ch ∈ [3] indicates that channel B image, ch ∈ [4] indicate dark
Channel image, ch ∈ [5] indicate bright channel image；I indicates gamut of chromaticities series, and value range is [0,255]；TL(i)chIt indicates
The target colorimetric grade value of the i-th gamut of chromaticities of channel image；OL(i)chIndicate the original colorimetric grade value of the i-th gamut of chromaticities of channel image；T table
Show the total chromatic value number of channel image；Effective gamut of chromaticities series of N expression channel image；N indicates that equal difference excavates series；K is indicated
Equal difference excavates limiting value, (n-1)≤K；The value self-setting of n and K.
S5. the target colorimetric grade value of each gamut of chromaticities of each channel image is normalized respectively, that is, distinguished
To each color of gray channel image, R channel image, G channel image, channel B image, dark channel image and bright channel image
The target colorimetric grade value of degree grade is normalized.
Normalized formula is as follows:
In formula, ch ∈ [0,1,2,3,4,5] indicates each channel image, specifically, ch ∈ [0] indicates gray channel image,
Ch ∈ [1] indicates R channel image, and ch ∈ [2] indicates G channel image, and ch ∈ [3] indicates that channel B image, ch ∈ [4] indicate dark
Channel image, ch ∈ [5] indicate bright channel image；I indicates gamut of chromaticities series, and value range is [0,255]；TL(i)chIt indicates
The target colorimetric grade value of the i-th gamut of chromaticities of channel image；Indicate the dot frequency value of the maximum chrominance grade of channel image；Indicate the dot frequency value of the minimal color grade of channel image；NL(i)chIndicate the target of the i-th gamut of chromaticities of channel image
Value after the normalization of gamut of chromaticities value.
S6. being respectively compared each channel image, (gray channel image, G channel image, channel B image, is helped secretly at R channel image
Road image and bright channel image) in each gamut of chromaticities normalization after target colorimetric grade value and original colorimetric grade value, obtain
The chrominance information of latent image.
In some embodiments, the chrominance information method for digging of latent image further include:
The original coloration spectrogram of each channel image is generated, and shows that its coloration composes relevant information, the coloration spectrum is related to be believed
Breath includes total pixel number, average color angle value, maximum chrominance grade, minimal color grade and effective spectral line number；
It generates each channel image and obtains the coloration spectrogram after stealthy image information, and show that its coloration composes relevant information,
The coloration spectrum relevant information includes total pixel number, average color angle value, maximum chrominance grade, minimal color grade and effective spectral line number.
The solution of the present invention and traditional scheme are compared below, specifically include low-light (level) image, high illumination image, water
The excavation of lower image and foggy image to the excavation of RGB channel coloration spectrum information and to RGB, dark bright channel coloration spectrum information,
In, take n=1 and n=4.
Related description: (1) present invention is precisely excavated using matlab R2018a software programming program progress image；(2) straight
Square graph coloring degree spectrum is drawn using matlab from tape function imhist (), uses 1 grade with the method for the present invention after normalizing
It is consistent precisely to excavate (original spectrum) effect；(3) it is compared with histogram equalization result, only using low-light (level) image as explanation.
The solution of the present invention and conventional histogram Contrast on effect
The excavation of the channel 1.RGB coloration spectrum information
(1) low-light (level) image.Low-light (level) image uses 950x533 sized images, and pixel value is distributed mainly on low gamut of chromaticities
On, such as 10 grades or less.
1. original image, gray channel image, R channel image, G channel image, channel B image are as shown in Figure 3.
2. each channel coloration spectrum Result of histogram is as shown in Figure 4.
After grey level histogram, chroma histogram excavate increase normalization, as a result imitated with 1 grade of accurate method for digging of the present invention
Fruit is consistent.(i.e. n=1) is precisely just excavated with 1 grade below and 4 grades are precisely excavated (i.e. n=4) effect and are compared.
3. it is as shown in Figure 5 that 1 grade of the present invention precisely excavates the accurate Result of each channel coloration spectrum.As can be seen from Figure 5
Gray channel image, R channel image, G channel image, human eye is seen in channel B image spectral line number are far smaller than actually active
Spectral line number, since vehicle body is white in image, theoretically breadth of spectrum line should be 0~255 section, however spectral line exists substantially in figure
10 grades hereinafter, the spectral line number of (stealth) can precisely be excavated more than 93%, as shown in table 1.
1 grade of 1 low-light (level) image RGB channel of table precisely excavates coloration spectrum information analytical table
4. it is as shown in Figure 6 that 4 grades of the present invention precisely excavates the accurate Result of each channel coloration spectrum.As can be seen from Figure 6
Stealthy image information is all precisely excavated in background image, and pixel number remains unchanged, and can completely be shown pair
Gamut of chromaticities information and image important information are answered, as shown in table 2.
4 grades of 2 low-light (level) image RGB channel of table precisely excavates coloration spectrum information analytical table
(2) high illumination image.High illumination image uses 402x328 sized images, and pixel value is distributed mainly on high chroma grade
On, such as 240 grades or more.
1. original image, gray channel image, R channel image, G channel image and channel B image are as shown in Figure 7.
2. each channel coloration spectrum Result of histogram is as shown in Figure 8.
After grey level histogram, chroma histogram excavate increase normalization, as a result imitated with 1 grade of accurate method for digging of the present invention
Fruit is consistent.It is just precisely excavated with 1 grade below and 4 grades of accurate mining effects is compared.
3. it is as shown in Figure 9 that 1 grade of the present invention precisely excavates the accurate Result of each channel coloration spectrum.As can be seen from Figure 9
Gray channel image, R channel image, G channel image, human eye is seen in channel B image spectral line number are far smaller than actually active
Spectral line number, since there are also tree and dash areas in image, theoretically breadth of spectrum line should be tens to 255 sections, however in figure
Spectral line is substantially at 240 grades or more.The spectral line number that can precisely excavate is more than 96%, as shown in table 3.
1 grade of 3 high illumination image RGB channel of table precisely excavates coloration spectrum information analytical table
4. it is as shown in Figure 10 that 4 grades of the present invention precisely excavates the accurate Result of each channel coloration spectrum.It can from Figure 10
Image information stealthy in background image is all precisely excavated out, and pixel number remains unchanged, and can completely show
Corresponding gamut of chromaticities information and image important information, as shown in table 4.
4 grades of 4 high illumination image RGB channel of table precisely excavates coloration spectrum information analytical table
(3) underwater picture.Underwater picture uses 450x372 sized images, and pixel value is distributed mainly on middle gamut of chromaticities, no
A not region is concentrated on channel gamut of chromaticities.
1. original color image, gray channel image, R channel image, G channel image, channel B image are as shown in figure 11.
2. each channel coloration spectrum Result of histogram is as shown in figure 12.
After grey level histogram, chroma histogram excavate increase normalization, as a result imitated with 1 grade of accurate method for digging of the present invention
Fruit is consistent.It is just precisely excavated with 1 grade below and 4 grades of accurate mining effects is compared.
3. it is as shown in figure 13 that 1 grade of the present invention precisely excavates the accurate Result of each channel coloration spectrum.It can from Figure 13
Gray scale channel image, R channel image, G channel image, the spectral line number that human eye is seen in channel B image far smaller than actually have out
Imitate spectral line number, since image is similar there are one layer of background colour in water, spectral line also can Relatively centralized, the spectral line number that can precisely excavate
It can reach 50% or so, as shown in table 5.
1 grade of 5 underwater picture RGB channel of table precisely excavates coloration spectrum information analytical table
4. it is as shown in figure 14 that 4 grades of the present invention precisely excavates the accurate Result of each channel coloration spectrum.It can from Figure 14
Image information stealthy in background image is all precisely excavated out, and pixel number remains unchanged, and can completely show
Corresponding gamut of chromaticities information and image important information, as shown in table 6.
4 grades of 6 underwater picture RGB channel of table precisely excavates coloration spectrum information analytical table
(4) foggy image.Foggy image uses 950x533 sized images, and pixel value is mainly evenly distributed.
1. original color image, gray channel image, R channel image, G channel image, channel B image are as shown in figure 15.
2. each channel coloration spectrum Result of histogram is as shown in figure 16.
After grey level histogram, chroma histogram excavate increase normalization, as a result imitated with 1 grade of accurate method for digging of the present invention
Fruit is consistent.It is just precisely excavated with 1 grade below and 4 grades of accurate mining effects is compared.
3. it is as shown in figure 17 that 1 grade of the present invention precisely excavates the accurate Result of each channel coloration spectrum.It can from Figure 17
Gray scale channel image, R channel image, G channel image, the spectral line number that human eye is seen in channel B image are less than actually active spectrum out
Line number, although stronger, the abundant information of the middle-level sense of image, the distribution of spectral line range is wide, and the spectral line still remained in 20% is hidden
Shape, as shown in table 7.
1 grade of 7 foggy image RGB channel of table precisely excavates coloration spectrum information analytical table
4. it is as shown in figure 18 that 4 grades of the present invention precisely excavates the accurate Result of each channel coloration spectrum.It can from Figure 18
Image information stealthy in background image is all precisely excavated out, and pixel number remains unchanged.It can completely show
Corresponding gamut of chromaticities information and image important information, as shown in table 8.
4 grades of 8 foggy image RGB channel of table precisely excavates coloration spectrum information analytical table
2.RGB coloration spectrum information in channel dark/bright precisely excavates
It precisely excavates difference with RGB channel coloration spectrum information to be first to do channel image conversion process dark/bright, this is this hair
A bright innovation, can be used for image prior information analysis.
By taking underwater picture as an example, underwater picture uses 450x372 sized images, and pixel value is distributed mainly on middle gamut of chromaticities
On, different channel gamut of chromaticities concentrate on a not region.
1. original color image, gray channel image, dark channel image, bright channel image are as shown in figure 19.
2. each channel coloration spectrum Result of histogram is as shown in figure 20.
3. it is as shown in figure 21 that 1 grade of the present invention precisely excavates the accurate Result of each channel coloration spectrum.It can from Figure 21
Gray scale channel image, R channel image, G channel image, the spectral line number that human eye is seen in channel B image far smaller than actually have out
Spectral line number is imitated, since image is similar there are one layer of background colour in water, spectral line also can Relatively centralized.The spectral line number that can precisely excavate
It can reach 50% or so, as shown in table 9.
Precisely excavate coloration spectrum information analytical table in 1 grade of the channel dark/bright 9 underwater picture RGB of table
4. it is as shown in figure 22 that 4 grades of the present invention precisely excavates the accurate Result of each channel coloration spectrum.It can from Figure 22
Image information stealthy in background image is all precisely excavated out, and pixel number remains unchanged.It can completely show
Corresponding gamut of chromaticities information and image important information, as shown in table 10.
Precisely excavate coloration spectrum information analytical table in 4 grades of the channel dark/bright 10 underwater picture RGB channel RGB of table
The solution of the present invention and conventional histogram equalize Contrast on effect
Only the method for the present invention with low-light (level) image under rgb space and conventional histogram equalization processing result compare for
Example, using 950x533 sized images, pixel value is distributed mainly on low gamut of chromaticities, and such as 10 grades or less.
1. original image, histogram-equalized image, gray channel image, R channel image, G channel image, channel B figure
As shown in figure 23.
2. each channel coloration of the invention composes accurate Result and conventional histogram equalizes Comparative result such as Figure 24~27
It is shown.
As can be seen that 4 grades are precisely excavated, can to excavate gray channel image, R channel image, G logical from Figure 24~27
The hide information that human eye can not be seen in road image, channel B image.It is available can to highlight part for the method for histogram equalization
Information has certain enhancing function to image, but image is not true image.Artificial changes the original letter of image
Breath, spectral line number are greatly reduced, and average color angle value significantly artificially increases, and image spectral line range narrows, and image hierarchy sense is poor.It is former
There are the pixels of gamut of chromaticities without such as the value of R, G, channel B gamut of chromaticities in 10~90 ranges, originally without gamut of chromaticities for this
Pixel artificially increase, such as the channel R 203~220, the channel G 194~211, channel B 113~196, as shown in table 11.
The accurate method for digging of table 11 and conventional histogram equalization processing method coloration compose comparison information analytical table
The above is only a preferred embodiment of the present invention, it should be understood that the present invention is not limited to described herein
Form should not be regarded as an exclusion of other examples, and can be used for other combinations, modifications, and environments, and can be at this
In the text contemplated scope, modifications can be made through the above teachings or related fields of technology or knowledge.And those skilled in the art institute into
Capable modifications and changes do not depart from the spirit and scope of the present invention, then all should be in the protection scope of appended claims of the present invention
It is interior.
Claims (9)
1. the chrominance information method for digging of latent image characterized by comprising
Obtain original image；
Original image is obtained respectively in the channel image of each image channel；
Calculate separately the dot frequency of each gamut of chromaticities of each channel image and effective gamut of chromaticities in each channel；
The target colorimetric grade value of each gamut of chromaticities of each channel image is calculated separately, the calculation formula of target colorimetric grade value is such as
Under:
In formula, ch indicates channel image；I indicates gamut of chromaticities series, and value range is [0,255]；TL(i)chIndicate channel figure
As the target colorimetric grade value of the i-th gamut of chromaticities；OL(i)chIndicate the original colorimetric grade value of the i-th gamut of chromaticities of channel image；T indicates channel
The total chromatic value number of image；Effective gamut of chromaticities series of N expression channel image；N indicates that equal difference excavates series；K indicates that equal difference is dug
Dig limiting value, (n-1)≤K；
The target colorimetric grade value of each gamut of chromaticities of each channel image is normalized respectively, normalized formula is such as
Under:
In formula, ch indicates channel image；I indicates gamut of chromaticities series, and value range is [0,255]；TL(i)chIndicate channel figure
As the target colorimetric grade value of the i-th gamut of chromaticities；Indicate the dot frequency value of the maximum chrominance grade of channel image；
Indicate the dot frequency value of the minimal color grade of channel image；NL(i)chIndicate the target colorimetric grade of the i-th gamut of chromaticities of channel image
Value after value normalization；
The target colorimetric grade value and original colorimetric grade value being respectively compared in each channel image after the normalization of each gamut of chromaticities, obtain
To the chrominance information of latent image.
2. the chrominance information method for digging of latent image according to claim 1, which is characterized in that the channel image packet
Gray channel image, R channel image, G channel image and channel B image are included, the calculation formula of target colorimetric grade value and is returned at this time
In one formula changed, ch ∈ [0,1,2,3] respectively indicates gray channel image, R channel image, G channel image and channel B figure
Picture.
3. the chrominance information method for digging of latent image according to claim 1, which is characterized in that the channel includes ash
Channel image, R channel image, G channel image, channel B image, dark channel image and bright channel image are spent, at this time target colorimetric
In the calculation formula and normalized formula of grade value, ch ∈ [0,1,2,3,4,5] respectively indicates gray channel image, the channel R figure
Picture, G channel image, channel B image, dark channel image and bright channel image.
4. the chrominance information method for digging of latent image according to claim 3, which is characterized in that the dark channel image
Acquisition methods are as follows: calculate each pixel of original image correspond to the minimum in R channel image, G channel image and channel B image
Chromatic value is stored in the image of corresponding pixel points identical with original image size position.
5. the chrominance information method for digging of latent image according to claim 4, which is characterized in that the dark channel image
Object pixel point value calculation formula it is as follows:
T(x,y)'ch=Min (O (x, y)ch)
In formula, ch ∈ [1,2,3] respectively indicates R channel image, G channel image and channel B image；O(x,y)chIndicate channel figure
The original pixels point value of picture；T(x,y)'chIndicate the object pixel point value of dark channel image.
6. the chrominance information method for digging of latent image according to claim 3, which is characterized in that the bright channel image
Acquisition methods are as follows: calculate each pixel of original image correspond to the maximum in R channel image, G channel image and channel B image
Chromatic value is stored in the image of corresponding pixel points identical with original image size position.
7. the chrominance information method for digging of latent image according to claim 6, which is characterized in that the bright channel image
Object pixel point value calculation formula it is as follows:
T(x,y)”ch=Max (O (x, y)ch)
In formula, ch ∈ [1,2,3] respectively indicates R channel image, G channel image and channel B image；O(x,y)chIndicate channel figure
The original pixels point value of picture；T(x,y)"chIndicate the object pixel point value of bright channel image.
8. the chrominance information method for digging of latent image according to claim 1, which is characterized in that the coloration of latent image
Information mining method further include:
The original coloration spectrogram of each channel image is generated, and shows that its coloration composes relevant information；
It generates each channel image and obtains the coloration spectrogram after stealthy image information, and show that its coloration composes relevant information.
9. the chrominance information method for digging of latent image according to claim 8, which is characterized in that the coloration spectrum is related
Information includes total pixel number, average color angle value, maximum chrominance grade, minimal color grade and effective spectral line number.
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