CN108765246A - A kind of selection method of steganographic system carrier image - Google Patents

A kind of selection method of steganographic system carrier image Download PDF

Info

Publication number
CN108765246A
CN108765246A CN201810285951.6A CN201810285951A CN108765246A CN 108765246 A CN108765246 A CN 108765246A CN 201810285951 A CN201810285951 A CN 201810285951A CN 108765246 A CN108765246 A CN 108765246A
Authority
CN
China
Prior art keywords
image
histogram
plane
value
bit
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
Application number
CN201810285951.6A
Other languages
Chinese (zh)
Other versions
CN108765246B (en
Inventor
陈贞佐
叶勇超
戴洪珠
杨任尔
陈计
赵萌
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ningbo University
Original Assignee
Ningbo University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Ningbo University filed Critical Ningbo University
Priority to CN201810285951.6A priority Critical patent/CN108765246B/en
Publication of CN108765246A publication Critical patent/CN108765246A/en
Application granted granted Critical
Publication of CN108765246B publication Critical patent/CN108765246B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G06T7/41—Analysis of texture based on statistical description of texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/0007—Image acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Probability & Statistics with Applications (AREA)
  • Facsimile Image Signal Circuits (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)

Abstract

A kind of selection method of the carrier image of steganographic system of the present invention, it is characterised in that include the following steps：Step 1: ten dimensional feature parameters of selected digital image；Step 2: to the mean value of image, variance, entropy, Texture complication, plane complicated degree LSBof α of image lowest order, each bit plane complexity plane value AVEof α of image, this six characteristic parameter normalizeds；Step 3: to histogram smoothness hsmoo, histogram full swing coefficient hmaxoc, histogram maximum value hmax, the minimum 0/1 deviation ratio Derate of bit plane bit stream of image, this four characteristic parameters make segmentation normalized；Step 4: extracting ten dimensional feature parameters of carrier image to be selected, and its feature radar map is drawn, Step 5: according to the feature radar map of more pair carrier images to be selected, selects suitable carrier image to be selected for the carrier image of steganographic system.Compared with the prior art, the advantages of the present invention are as follows：Being judged by the distribution of the radar map to image and shape can quick and precisely suitable carrier image.

Description

A kind of selection method of steganographic system carrier image
Technical field
The present invention relates to a kind of selection methods of steganographic system carrier image.
Background technology
Steganography is novel Information hiding skill secret information being embedded into the multimedias such as text, sound, image, video Art, but aesthetic quality and the statistical nature of carrier image are not significantly changed.Carrier image, steganography insertion scheme, secret Information is three elements of steganographic system, and steganographic system performance indicator can be influenced by these three elements.According to previous research The result shows that steganography performance index quality is by the multiple characteristic parameter joint effects of carrier image, and steganographic algorithm is different, therewith Closely related carrier image characteristic parameter also can be different.Image be combined by the pixel arrangement of inherent similitude and order and At with prodigious stochastic behaviour and huge information content, therefore selection is suitable and has the carrier image of significant complexity It is the problem of tool acquires a certain degree of difficulty.
Invention content
The technical problem to be solved by the present invention is to provide a kind of steganographic system carrier image for the above-mentioned prior art Selection method, the selection method can be the distributions of the carrier image characteristic parameter closely related with steganography characteristic performance index Visual and clear to reflect, then being judged by the distribution of the radar map to image and shape can quick and precisely suitable carrier figure Picture.
Technical solution is used by the present invention solves above-mentioned technical problem：A kind of selection of the carrier image of steganographic system Method, it is characterised in that include the following steps：
Step 1: ten dimensional feature parameters of selected digital image, they are respectively：Mean value, variance, entropy, Texture complication, histogram Figure smoothness hsmoo, histogram full swing coefficient hmaxoc, histogram maximum value hmax, the minimum bit plane bit stream of image Plane complicated degree LSBof α of 0/1 deviation ratio Derate, image lowest order, each bit plane complexity plane value AVEof α of image； Wherein mean value is the average brightness of image；Variance is the rich palette degree of image；Entropy indicates the bit of image gray levels set Average, per bit/pixel also illustrate the average information of video source；Image texture complexity reflects image sky Between texture complexity situation；In order to define the smoothness h of histogramsmoo, full swing coefficient hmaxocAnd histogram is maximum Value hmax, first have to each the point definition oscillation coefficient h for defining histogram functions h (n) and histogramoc[n] (wherein n ∈ { 0,1 ..., 255 }, for M × N images, histogram functions are denoted as h (n), and wherein n indicates the value of pixel, for gray level image For, n ∈ { 0,1 ..., 255 }, it is expressed as the frequency that gray value in image is n, i.e.,：
Wherein：
So vibrate coefficient hoc[n] (wherein n ∈ 0,1 ..., 255 }) be：
According to above formula, then having：
Histogram smoothness
The full swing coefficient h of histogrammaxoc=Max (hoc[n])；
The maximum value h of histogrammax=Max (h [n])；
The minimum 0/1 deviation ratio Derate=of bit plane bit stream of image | r-0.5 |, wherein r indicates 0 or 1 in bit stream In shared ratio；
Remember KNUM(B → W) is that bianry image overturns number along columns and rows black and white, remembers MAXNUM(B → W) is bianry image by row Theoretically maximum with row black and white overturns number, then 8 bit plane complexity formula for gray level image are as follows：Remember that LSBof α are the complexity of the minimum bit plane of image, AVEof α are eight bit planes of gray level image Average complexity；
Step 2: to the mean value of image, variance, entropy, Texture complication, plane complicated degree LSBof α of image lowest order, figure As each bit plane complexity plane value AVEof α, this six characteristic parameter normalizeds；
Step 3: setting threshold value t, to histogram smoothness hsmoo, histogram full swing coefficient hmaxoc, histogram Maximum value hmax, the minimum 0/1 deviation ratio Derate of bit plane bit stream of image, this four characteristic parameters make segmentation normalized, Segmentation normalized formula be：
By histogram smoothness hsmoo, histogram full swing coefficient hmaxoc, histogram maximum value hmax, image most 0/1 deviation ratio Derate of low level plane bit stream seeks the y values after segmentation normalized respectively as the x in formula；
Step 4: carrier image to be selected to be extracted to its ten dimensional feature parameters according to step 1, by carrier image to be selected Plane complicated degree LSBof α of mean value, variance, entropy, Texture complication, image lowest order, each bit plane complexity plane value of image AVEof α are normalized according to step 2, by the histogram smoothness h of carrier image to be selectedsmoo, histogram maximum Vibrate coefficient hmaxoc, histogram maximum value hmax, the minimum 0/1 deviation ratio Derate of bit plane bit stream of image is according to step 3 Segmentation normalized is carried out, the value after then ten characteristic parameters are normalized is waited for according to drawing clockwise Select the feature radar map of carrier image；
Step 5: according to the feature radar map of more pair carrier images to be selected, histogram smoothness h is selectedsmoo, histogram Full swing coefficient hmaxoc, histogram maximum value hmax, the minimum 0/1 deviation ratio Derate of bit plane bit stream of image is small, image Lowest order it is plane complicated degree LSBof α, each bit plane complexity plane value AVEof α high of image carrier image to be selected be steganography The carrier image of system.
Compared with the prior art, the advantages of the present invention are as follows：The carrier image characteristic parameter radar map that the present invention designs, energy It reflects the distribution of the carrier image characteristic parameter closely related with steganography characteristic performance index is visual and clear, as Machinery instrument can reflect a complex power system working order equally, during steganography, secret information insertion person Can rule of thumb, being judged by the distribution of the radar map to image and shape can quick and precisely suitable carrier image.
Description of the drawings
Fig. 1 is No. 1 image in the embodiment of the present invention.
Fig. 2 is the feature radar map of No. 1 image in the embodiment of the present invention.
Fig. 3 is No. 2 images in the embodiment of the present invention.
Fig. 4 is the feature radar map of No. 2 images in the embodiment of the present invention.
Specific implementation mode
Below in conjunction with attached drawing embodiment, present invention is further described in detail.
The selection method of the carrier image of steganographic system provided in this embodiment comprising following steps：
Step 1: ten dimensional feature parameters of selected digital image, they are respectively：Mean value, variance, entropy, Texture complication, histogram Figure smoothness hsmoo, histogram full swing coefficient hmaxoc, histogram maximum value hmax, the minimum bit plane bit stream of image Plane complicated degree LSBof α of 0/1 deviation ratio Derate, image lowest order, each bit plane complexity plane value AVEof α of image；
Wherein mean value Mean illustrates the average brightness of piece image；Variance Variance indicates that picture tone enriches journey Degree；Entropy Entropy illustrate image contain information content number, for gray level image C, pixel value be i appearance it is general Rate is pi, i=0,1 ..., 255, then piece image entropy Entropy values are defined as：
Image texture complexity Complexity reflects the situation of the texture complexity of image space, for M × N images, cijIndicate the pixel value of the i-th row jth row pixel, the value of i is 1,2 ... the value of M, j is 1,2 ... N, its definition It is as follows：
In order to define the smoothness h of histogramsmoo, full swing coefficient hmaxocAnd histogram highest value hmax, first have to Define each point definition oscillation coefficient h of histogram functions h (n) and histogramoc[n] (wherein n ∈ { 0,1 ..., 255 }, For M × N images, gray level image is converted thereof into, histogram functions are denoted as h (n), and wherein n indicates the value of pixel, for ash It spends for image, n ∈ { 0,1 ..., 255 }, it is expressed as the frequency that gray value in image is n, i.e.,：
Wherein：
So vibrate coefficient hoc[n] (wherein n ∈ 0,1 ..., 255 }) be：
According to above formula, then having：
Histogram smoothness
The full swing coefficient h of histogrammaxoc=Max (hoc[n])；
The maximum value h of histogrammax=Max (h [n])；
The minimum 0/1 deviation ratio Derate=of bit plane bit stream of image | r-0.5 |, wherein r indicates 0 or 1 in bit stream In shared ratio；
Remember KNUM(B → W) is that bianry image overturns number along columns and rows black and white, remembers MAXNUM(B → W) is bianry image by row Theoretically maximum with row black and white overturns number, then 8 bit plane complexity formula for gray level image are as follows：Remember that LSBof α are the complexity of the minimum bit plane of image, AVEof α are eight bit planes of gray level image Average complexity；
Step 2: to the mean value of image, variance, entropy, Texture complication, plane complicated degree LSBof α of image lowest order, figure As each bit plane complexity plane value AVEof α, this six characteristic parameter normalizeds, the formula of normalized is：Wherein x refers to some image features value before normalization, and y refers to characteristics of image ginseng after normalization Several values, maxx and minx are preset constant, and for each characteristic parameter, the numerical value of maxx and minx is different；The present embodiment In, maxx is the maximum value of this feature parameter of all images in the standard gallery BOSSbase1.01 in steganographic system, minx It is the minimum value of this feature parameter of all images in standard gallery BOSSbase1.01；
Step 3: setting threshold value t, threshold value t is parameter preset, to histogram smoothness hsmoo, histogram full swing Coefficient hmaxoc, histogram maximum value hmax, the minimum 0/1 deviation ratio Derate of bit plane bit stream of image, this four characteristic parameters Make segmentation normalized, the formula for being segmented normalized is：
By histogram smoothness hsmoo, histogram full swing coefficient hmaxoc, histogram maximum value hmax, image most 0/1 deviation ratio Derate of low level plane bit stream seeks the y values after segmentation normalized respectively as the x in formula；
Step 4: carrier image to be selected to be extracted to its ten dimensional feature parameters according to step 1, by carrier image to be selected Plane complicated degree LSBof α of mean value, variance, entropy, Texture complication, image lowest order, each bit plane complexity plane value of image AVEof α are normalized according to step 2, by the histogram smoothness h of carrier image to be selectedsmoo, histogram maximum Vibrate coefficient hmaxoc, histogram maximum value hmax, the minimum 0/1 deviation ratio Derate of bit plane bit stream of image is according to step 3 Segmentation normalized is carried out, the value after then ten characteristic parameters are normalized is waited for according to drawing clockwise Select the feature radar map of carrier image；
Step 5: according to the feature radar map of more pair carrier images to be selected, histogram smoothness h is selectedsmoo, histogram Full swing coefficient hmaxoc, histogram maximum value hmax, the minimum 0/1 deviation ratio Derate of bit plane bit stream of image it is small, image Plane complicated degree LSBof α of lowest order, each bit plane complexity plane value AVEof α of image big carrier image to be selected is steganography The carrier image of system.
It in this step, needs to test a large amount of carrier images, finds out the feature ginseng for the carrier image for being suitable as steganographic system Common trait possessed by number radar map：Histogram smoothness hsmoo, histogram full swing coefficient hmaxoc, histogram most Big value hmax, the minimum 0/1 deviation ratio Derate of bit plane bit stream of image it is small, plane complicated degree LSBof α of image lowest order, image Each bit plane complexity plane value AVEof α are big.
Such as histogram profit and loss compensation steganography method, histogram smoothness hsmoo, histogram full swing Coefficient hmaxoc, histogram maximum value hmax, the minimum 0/1 deviation ratio Derate of bit plane bit stream of image it is less than normal, image lowest order Plane complicated degree LSBof α, each bit plane complexity plane value AVEof α of image higher carrier image is preferable carrier figure Picture, and for mean value, variance, entropy, Texture complication, then there is no limit for this four characteristic parameters；
Attached drawing 2 is the feature radar map of No. 1 image obtained using method provided by the invention, and attached drawing 4 is to use this hair The feature radar map for No. 2 images that the method for bright offer obtains, for the steganography method compensated based on histogram profit and loss, 1 Number image belongs to performance preferably carrier image, its main feature is that having smaller hmax、hmaxoc、hsmooValue, Derate is similar to Zero, the plane complicated degree of complexity and average bit of lowest order is higher, therefore the anti-statistic mixed-state of steganographic system and safety Also higher, and the performance of No. 2 images is poor, image has larger hmax、hmaxoc、hsmoo, Derate value, and texture is complicated Degree, the complexity of lowest order and the plane complicated degree of average bit smaller, the anti-statistic mixed-state of corresponding steganographic system and safety Also poor.

Claims (1)

1. a kind of selection method of the carrier image of steganographic system, it is characterised in that include the following steps：
Step 1: ten dimensional feature parameters of selected digital image, they are respectively：Mean value, variance, entropy, Texture complication, histogram are flat Slippery hsmoo, histogram full swing coefficient hmaxoc, histogram maximum value hmax, the minimum bit plane bit stream of image 0/1 partially Plane complicated degree LSBof α of rate Derate, image lowest order, each bit plane complexity plane value AVEof α of image；Wherein Value is the average brightness of image；Variance is the rich palette degree of image；Entropy indicates the bit average of image gray levels set, Per bit/pixel also illustrates the average information of video source；Image texture complexity reflects the texture of image space Complicated situation；In order to define the smoothness h of histogramsmoo, full swing coefficient hmaxocAnd histogram highest value hmax, first First to define each point definition oscillation coefficient h of histogram functions h (n) and histogramoc[n] (wherein n ∈ 0,1 ..., 255 }, for M × N images, histogram functions are denoted as h (n), and wherein n indicates the value of pixel, for gray level image, n ∈ { 0,1 ..., 255 }, it is expressed as the frequency that gray value in image is n, i.e.,：
Wherein：
So vibrate coefficient hoc[n] (wherein n ∈ 0,1 ..., 255 }) be：
According to above formula, then having：
Histogram smoothness
The full swing coefficient h of histogrammaxoc=Max (hoc[n])；
The maximum value h of histogrammax=Max (h [n])；
The minimum 0/1 deviation ratio Derate=of bit plane bit stream of image | r-0.5 |, wherein r indicates 0 or 1 institute in the bitstream The ratio accounted for；
Remember KNUM(B → W) is that bianry image overturns number along columns and rows black and white, remembers MAXNUM(B → W) be bianry image in rows and columns The theoretically maximum overturning number of black and white, then 8 bit plane complexity formula for gray level image are as follows：Remember that LSBof α are the complexity of the minimum bit plane of image, AVEof α are eight bit planes of gray level image Average complexity；
Step 2: each to the mean value of image, variance, entropy, Texture complication, plane complicated degree LSBof α of image lowest order, image A bit plane complexity plane value AVEof α, this six characteristic parameter normalizeds；
Step 3: setting threshold value t, to histogram smoothness hsmoo, histogram full swing coefficient hmaxoc, histogram maximum Value hmax, the minimum 0/1 deviation ratio Derate of bit plane bit stream of image, this four characteristic parameters make segmentation normalized, are segmented The formula of normalized is：
By histogram smoothness hsmoo, histogram full swing coefficient hmaxoc, histogram maximum value hmax, image lowest order 0/1 deviation ratio Derate of plane bit stream seeks the y values after segmentation normalized respectively as the x in formula；
Step 4: carrier image to be selected to be extracted to its ten dimensional feature parameters according to step 1, by the mean value of carrier image to be selected, Plane complicated degree LSBof α of variance, entropy, Texture complication, image lowest order, each bit plane complexity plane value AVEof of image α is normalized according to step 2, by the histogram smoothness h of carrier image to be selectedsmoo, histogram full swing system Number hmaxoc, histogram maximum value hmax, the minimum 0/1 deviation ratio Derate of bit plane bit stream of image divided according to step 3 Section normalized, value after then ten characteristic parameters are normalized according to drawing carrier to be selected clockwise The feature radar map of image；
Step 5: according to the feature radar map of more pair carrier images to be selected, histogram smoothness h is selectedsmoo, histogram maximum Vibrate coefficient hmaxoc, histogram maximum value hmax, the minimum 0/1 deviation ratio Derate of bit plane bit stream of image it is small, image is minimum The carrier image to be selected big each bit plane complexity plane value AVEof α of bit plane complexity LSBof α, image is steganographic system Carrier image.
CN201810285951.6A 2018-04-03 2018-04-03 A kind of selection method of steganographic system carrier image Active CN108765246B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810285951.6A CN108765246B (en) 2018-04-03 2018-04-03 A kind of selection method of steganographic system carrier image

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810285951.6A CN108765246B (en) 2018-04-03 2018-04-03 A kind of selection method of steganographic system carrier image

Publications (2)

Publication Number Publication Date
CN108765246A true CN108765246A (en) 2018-11-06
CN108765246B CN108765246B (en) 2019-07-16

Family

ID=63980667

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810285951.6A Active CN108765246B (en) 2018-04-03 2018-04-03 A kind of selection method of steganographic system carrier image

Country Status (1)

Country Link
CN (1) CN108765246B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109785221A (en) * 2019-01-11 2019-05-21 宁波大学 A kind of digital picture steganography method and secret information extraction method
CN110992357A (en) * 2019-12-17 2020-04-10 云南中烟工业有限责任公司 Radar chart analysis method, medium and single chip microcomputer for storing radar chart analysis method and respective applications

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102075283A (en) * 2010-12-06 2011-05-25 深圳大学 Information steganography method and device
US20130336525A1 (en) * 2012-06-19 2013-12-19 Andrew F. Kurtz Spectral edge marking for steganography or watermarking
CN105260981A (en) * 2015-10-08 2016-01-20 宁波大学 Optimal coupling image steganography method based on packet replacement
CN107563155A (en) * 2017-08-08 2018-01-09 中国科学院信息工程研究所 A kind of safe steganography method and device based on generation confrontation network
CN107622469A (en) * 2017-07-21 2018-01-23 南京信息工程大学 Image carrier-free information concealing method based on quaternion wavelet conversion
CN107689026A (en) * 2017-08-24 2018-02-13 中国科学技术大学 Reversible steganography method based on optimum code

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102075283A (en) * 2010-12-06 2011-05-25 深圳大学 Information steganography method and device
US20130336525A1 (en) * 2012-06-19 2013-12-19 Andrew F. Kurtz Spectral edge marking for steganography or watermarking
CN105260981A (en) * 2015-10-08 2016-01-20 宁波大学 Optimal coupling image steganography method based on packet replacement
CN107622469A (en) * 2017-07-21 2018-01-23 南京信息工程大学 Image carrier-free information concealing method based on quaternion wavelet conversion
CN107563155A (en) * 2017-08-08 2018-01-09 中国科学院信息工程研究所 A kind of safe steganography method and device based on generation confrontation network
CN107689026A (en) * 2017-08-24 2018-02-13 中国科学技术大学 Reversible steganography method based on optimum code

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
杨任尔等: ""基于图像特征的隐写术载体图像的选择"", 《光电子·激光》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109785221A (en) * 2019-01-11 2019-05-21 宁波大学 A kind of digital picture steganography method and secret information extraction method
CN109785221B (en) * 2019-01-11 2023-01-03 宁波大学 Digital image steganography method and secret information extraction method
CN110992357A (en) * 2019-12-17 2020-04-10 云南中烟工业有限责任公司 Radar chart analysis method, medium and single chip microcomputer for storing radar chart analysis method and respective applications
CN110992357B (en) * 2019-12-17 2023-04-14 云南中烟工业有限责任公司 Radar chart analysis method, medium and single chip microcomputer for storing radar chart analysis method and respective application

Also Published As

Publication number Publication date
CN108765246B (en) 2019-07-16

Similar Documents

Publication Publication Date Title
CN108681991A (en) Based on the high dynamic range negative tone mapping method and system for generating confrontation network
CN111145116B (en) Sea surface rainy day image sample augmentation method based on generation of countermeasure network
CN111899205B (en) Image enhancement method of scene self-adaptive wide dynamic infrared thermal imaging
CN112614077B (en) Unsupervised low-illumination image enhancement method based on generation countermeasure network
CN107578451A (en) A kind of adaptive key color extraction method towards natural image
CN109783182A (en) A kind of method of adjustment, device, equipment and the medium of page subject matter tone
US20110050723A1 (en) Image processing apparatus and method, and program
CN107657619A (en) A kind of low-light (level) Forest fire image dividing method
CN108765246B (en) A kind of selection method of steganographic system carrier image
CN105844213B (en) Green fruit recognition method
CN109472757A (en) It is a kind of that logo method is gone based on the image for generating confrontation neural network
CN108510562A (en) Digital camouflage method for generating pattern based on image fractal texture
CN106709504A (en) Detail-preserving high fidelity tone mapping method
CN107133920A (en) A kind of automatic generation method of the mosaic of view-based access control model feature
CN104794726B (en) A kind of underwater picture Parallel segmentation method and device
CN106485266A (en) A kind of ancient wall classifying identification method based on extraction color characteristic
CN103049754B (en) The picture recommendation method of social networks and device
CN108550124B (en) Illumination compensation and image enhancement method based on bionic spiral
JP5615344B2 (en) Method and apparatus for extracting color features
CN109194846A (en) A kind of EMD (n, m, δ) adapting to image steganographic method based on complexity
CN102930289A (en) Method for generating mosaic picture
Dube et al. Hybrid approach to enhance contrast of image for forensic investigation using segmented histogram
Park et al. Applying enhanced confusion line color transform using color segmentation for mobile applications
CN108876721A (en) Super-resolution image reconstruction method and system based on course learning
CN105631812B (en) Control method and control device for color enhancement of display image

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant