CN1303432C - Remote sensing image picture element and characteristic combination optimizing mixing method - Google Patents
Remote sensing image picture element and characteristic combination optimizing mixing method Download PDFInfo
- Publication number
- CN1303432C CN1303432C CNB031290566A CN03129056A CN1303432C CN 1303432 C CN1303432 C CN 1303432C CN B031290566 A CNB031290566 A CN B031290566A CN 03129056 A CN03129056 A CN 03129056A CN 1303432 C CN1303432 C CN 1303432C
- Authority
- CN
- China
- Prior art keywords
- image
- opt
- wavelet
- coefficient
- formula
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Abstract
The present invention relates to a remote sensing image pixel and characteristic combination optimal merging method. In the method, in IHS space, the optimal pixel grade merging of two characteristic indexes of space detail information and spectral information is carried out to low-frequency baseband coefficients obtained through intensity components of multiple spectrum images through wavelet multilayer decomposition and low-frequency baseband coefficients obtained through high resolution images through corresponding multilayer wavelet decomposition; characteristic grade merging is carried out to high frequency coefficients through wavelet decomposition; then, corresponding wavelet inverse transform is carried out to wavelet coefficients for obtaining new intensity components; IHS reverse transformation is carried out for obtaining merged images. The present invention combines the characteristics of the IHS merging method and the wavelet merging method, causes merged images not only to reach maximum spatial resolution, but also to reduce colored distortion in the maximum degree by carrying out pixel grade optimal merging and the characteristic grade merging of high frequency sub band coefficients respectively to weight coefficients of wavelet baseband coefficients, and effectively improves the spectral information indexes of merged images.
Description
Technical field:
The present invention relates to a kind of remote sensing image pixel and characteristic binding optimum fusion method, decompose time-frequency characteristic and IHS (intensity I ntensity-colourity Hue-saturation degree Saturation) the conversion fusion method that has in conjunction with the wavelet multiresolution rate, carry out Pixel-level and feature level associating optimum fusion, be the core technology that remote sensing image merges, in fields such as all kinds of military or civilian Remote Sensing Information Processing System, digital city space information system, all can be widely used.
Background technology:
The multispectral remote sensing image of panchromatic remote sensing image of effective fusion high resolving power and low resolution how, two characteristic indexs of space detailed information and spectral information in the balanced fusion results are one of multi-source remote sensing visual fusion Study on Technology focuses.
The IHS fusion method that people such as Haydn at first propose is one of classical practical algorithm.This method transforms to IHS space by the IHS conversion from rgb space with multispectral image, simultaneously high-resolution panchromatic image is carried out linear stretch, the feasible average of back image and the strength component I in variance and the IHS space of stretching
0Consistent.Then, the high resolution image after stretching as new strength component, is arrived former rgb space with H and S component according to IHS inverse transformation fortran.Like this, make that the image after merging had both had higher spatial resolution, kept former low resolution multispectral image identical colourity and saturation degree simultaneously again.Yet, the IHS fusion method of this classics exists certain defective, because the data of different-waveband have different spectral patterns, the IHS fusion method has been twisted original spectral characteristic, produced spectrum degradation phenomena in various degree, thereby be unfavorable for the correct identification and the classification of image, particularly for the visual fusion of the multisensor remote sensing image of phase simultaneously not, the IHS fusion method can't make the tone of the former multispectral image of color harmony that merges image be consistent, it is this because the conversion of spectral information has caused image can not be used for atural object identification and inverting.People such as Te-Ming have carried out mathematical proof in the IHS space, have discussed the defective of IHS fusion method, and the conclusion that obtains is: although be used to replace strength component I
0High-resolution panchromatic image I
NewBefore replacement, carried out the coupling of the statistical property of image, but matching error δ=I
New-I has caused colored distortion.
Summary of the invention:
The objective of the invention is to deficiency at above-mentioned IHS conversion integration technology, a kind of remote sensing image pixel and characteristic binding optimum fusion method are provided, can improve the spatial resolution that merges the back image, can reduce colored distortion again, effectively improve the spectral information index that merges image.
For realizing such purpose, the present invention is in the IHS space, the strength component of multispectral image is decomposed the low frequency base band coefficient that obtains through the small echo multilayer, obtaining low frequency base band coefficient with high resolution image through corresponding multilayer wavelet decomposition carries out merging with the optimum Pixel-level of spatial detail information and two characteristic indexs of spectral information, high frequency coefficient after wavelet decomposition is carried out the feature level to be merged, then wavelet coefficient is carried out corresponding wavelet inverse transformation, obtain new strength component, the image after carrying out again obtaining merging after the IHS inverse transformation.
Because wavelet transformation has good frequency division characteristic at transform domain, the statistical property of wavelet coefficient has reflected the notable features such as edge, line and zone of remote sensing image, and the present invention introduces multiresolution analysis (Multi-resolution Analysis) method of wavelet transformation in the fusion of multispectral remote sensing image of panchromatic remote sensing image of high resolving power and low resolution.
Method of the present invention comprises following concrete steps:
(1) multispectral image B is carried out the IHS conversion, obtain colourity H, saturation degree S and strength component I respectively, then I component is carried out 3 layers of wavelet decomposition in the IHS color space;
(2) high resolution image A is carried out linear stretch and histogram coupling, carry out wavelet decomposition then, decompose the number of plies and also be 3 layers;
(3) determine one 3 * 3 spatial domain window, respectively the I component of image B and the subband wavelet coefficient of image A are added up, try to achieve average μ (2
j) and variance D (2
j);
(4) the high-frequency sub-band coefficient of corresponding layers of resolution carries out the fusion of feature level according to (1) formula:
(1) in the formula, 2
jBe the wavelet decomposition number of plies, W
k(2
j, x is 2 y)
jThe high-frequency sub-band coefficient fusion results that obtains under the resolution; W
A k(2
j, x, y) and W
B k(2
j, x y) is respectively the corresponding high-frequency sub-band coefficient of the I component of image A and image B, D
A k, D
B kBe respectively so that (x is the variance of 3 * 3 spatial domain window of center pixel y), and (x y) is the coordinate of center pixel;
(5) to the low frequency base band coefficient of the subband wavelet coefficient of the I component of image B and image A, carry out optimum Pixel-level according to (2) formula and merge the k of weight coefficient
OptAsk excellent according to spectral information evaluation index and spatial resolution evaluation index:
A(2
j,x,y)=k
1A
A(2
j,x,y)+k
2A
B(2
j,x,y) (2)
(2) in the formula, A
A(2
j, x, y), A
B(2
j, x y) is respectively the correspondence 2 of the I component of image A and image B
jThe low frequency base band data of resolution, k
1, k
2For needs are asked excellent weight coefficient.According to normalization requirement, k
1, k
2Satisfy k
1+ k
2=1, promptly ask excellent Determination of Weight Coefficient can be summed up as the k that satisfies objective function
Opt=k
1=1-k
2
(6) to the low frequency base band coefficient of the wavelet coefficient of the Pixel-level optimum fusion that obtains, and each high frequency wavelet coefficient that the feature level of carrying out merges carries out corresponding wavelet inverse transformation, obtains new strength component I ';
(7) I ', H, S are carried out the IHS inverse transformation, the image C after obtaining merging.
The present invention combines the characteristics of IHS fusion method and small echo fusion method, ask the feature level of excellent fusion and high-frequency sub-band to merge by respectively the weight coefficient of small echo base band coefficient having been carried out Pixel-level, its beneficial effect is presented as: make the image after merging both reach the highest spatial resolution, reduced colored distortion simultaneously again to greatest extent.Balanced space detailed information and two characteristic indexs of spectral information in the fusion results have effectively been improved the spectral information index that merges image.
Description of drawings:
Fig. 1 is the present invention-based on the remote sensing images pixel and the characteristic binding optimal characteristics fusion method process flow diagram of wavelet statistic characteristics.
Fig. 2 is the contrast of remote sensing image fusion results of the present invention and IHS method.Wherein, Fig. 2 (a) is multispectral remote sensing image (512 * 512); Fig. 2 (b) is the panchromatic remote sensing image of high resolving power (512 * 512); Fig. 2 (c) is the fusion results (512 * 512) of IHS method, and Fig. 2 (d) is fusion results of the present invention (512 * 512).
Fig. 3 is that the feature level of each sub-band coefficients after the wavelet decomposition merges.Wherein, Fig. 3 (a)~(c) is respectively the horizontal high frequency imaging of panchromatic high resolution image (P-Panchromatic) and multispectral image (M-Multispectral) and feature level fusion results image (F-Fused); Fig. 3 (d)~(f) is respectively panchromatic, multispectral vertical high frequency image and feature level fusion results image thereof; Fig. 3 (g)~(i) is respectively panchromatic, multispectral oblique line high frequency imaging and feature level fusion results image thereof.
Fig. 4 is a performance evaluation index curve of the present invention.
Embodiment:
In order to understand technical scheme of the present invention better, be further described below in conjunction with the embodiment of accompanying drawing.
Detailed process of the present invention such as Fig. 1.The present invention chooses a multispectral remote sensing image B (512 * 512) as Fig. 2 (a), and the panchromatic remote sensing image A of high resolving power (512 * 512) behind the strict registration of A, B, implements following steps as Fig. 2 (b):
1, multispectral image B is carried out the IHS conversion, obtain colourity H, saturation degree S and strength component I respectively, then I component is carried out 3 layers of wavelet decomposition in the IHS color space;
2, high resolution image A is carried out linear stretch and histogram coupling, carry out wavelet decomposition then, decompose the number of plies and also be 3 layers;
3, determine one 3 * 3 spatial domain window, respectively the I component of image B and the subband wavelet coefficient of image A are added up, try to achieve average μ (2
j) and variance D (2
j);
4, determine the high-frequency sub-band coefficient of corresponding layers of resolution according to (1) formula, carry out the feature level and merge; Can obtain the feature level fusion results of corresponding layers of resolution, as shown in Figure 3; Fig. 3 is that the feature level of each sub-band coefficients after the wavelet decomposition merges.Fig. 3 (a)~(c) is respectively the horizontal high frequency imaging of panchromatic high resolution image (P-Panchromatic) and multispectral image (M-Multispectral) and feature level fusion results image (F-Fused); Fig. 3 (d)~(f) is respectively panchromatic, multispectral vertical high frequency image and feature level fusion results image thereof; Fig. 3 (g)~(i) is respectively panchromatic, multispectral oblique line high frequency imaging and feature level fusion results image thereof;
5, to the low frequency base band coefficient of the subband wavelet coefficient of the I component of image B and image A, carry out Pixel-level according to (2) formula and merge weight coefficient k
OptAsk excellent according to the objective function shown in (3) formula:
F(k
opt)=F(k
1,k
2)
=Max(E
SP(k
1,k
2);E
HF(k
1,k
2)) OR Max(E
SP(k
1,k
2);AG(k
1,k
2)) (3)
={E
SP(k
opt);E
HF(k
opt)}OR{E
SP(k
opt);AG(k
opt)}
D:g(k
opt):k
opt=k
1=1-k
2(0≤k
opt≤1)
k
opt∈D∈R
In the formula, E
SP(k
1, k
2) be the spectral information evaluation index, E
HF(k
1, k
2) be the spatial resolution evaluation index, AG (k
1, k
2) be the average gradient evaluation index, D:g (k
Opt) for asking the constraint definition territory of excellent objective function.
Wherein, fused images optimizing evaluation index is defined as follows respectively:
(i) spectral information evaluation index
Utilize the degree of correlation of fused images and multispectral image to define the evaluation index of spectral information.
Make that f is a fused image, f
0Be multispectral image.The definition spectral information is estimated E
SPIndex is as (4) formula.
Wherein, npix is the number of pixel in the image, f and f
0The gray average of presentation video, degree of correlation Corr (f, f
0) reflected image f and f
0Similarity degree.
(ii) spatial resolution evaluation index
Utilize the high fdrequency component and the degree of correlation between the high-definition picture high fdrequency component of the gray scale of fused images correspondence to come definition space resolution index.Make f
HBe the high resolving power panchromatic image.At first the image that merges is converted into gray level image, carries out wavelet decomposition then, obtain four component (f of fused images
a, f
h, f
v, f
d), represent the low frequency component of fused images, the high fdrequency component of horizontal direction, the high fdrequency component of vertical direction and the high fdrequency component of diagonal respectively.Equally also can obtain four component (f of high-definition picture wavelet decomposition
H a, f
H h, f
H v, f
H d).Definition space resolution evaluation index is shown in (5) formula:
In addition, the most direct index of improvement of image definition quality is the average gradient of image, it has reflected the readability of image, also reflects minor detail contrast and texture transformation feature in the image simultaneously, and definition average gradient (Average grads) evaluation index is shown in (6) formula:
(6) in the formula, and Δ xf (i, j), (i j) is respectively pixel (i, j) first order difference on the x/y direction to Δ yf;
Weight coefficient k according to the fusion of the base band data in the optimizing evaluation index correction fusion criterion of fused images
1, k
2Fusion weights k along with high resolution image low frequency base band coefficient
1Increase, spatial resolution evaluation index E
HFIncrease spectral information evaluation index E with AG thereupon
SPReduce.Therefore, the k in (3) formula
OptBe to make objective function reach maximum weight coefficient, promptly make E
SP, E
HFPerhaps E
SP, AG reaches simultaneously maximum weight coefficient.k
OptSpan is [0,1].
According to weight coefficient k
OptAsk excellent objective function, in interval [0,1], along with weight coefficient merges weights k
1Increase, obtain spatial resolution evaluation index E by (7) formula
HFWith AG and spectral information evaluation index E
SPCurve, intersections of complex curve is the k of optimum weight coefficient
Opt, as shown in Figure 4.
(7) in the formula, E (i) is an evaluation index, and i is weight coefficient k
1The number of times of optimizing.Figure 4 shows that the E that obtains with optimizing step-length 0.001 in [0,1]
SP, E
HF, the AG curve.Can see the E after the normalization
SP, E
HF, AG is non-linear, E
HFWith merging weights k
1Increase and the trend that increases greater than the trend of the increase of AG.Satisfy the weight coefficient k that asks excellent objective function
OptBe E
SPAnd E
HF, E
SPIntersection point with AG
Or
(convergence precision δ≤0.001).Therefore, k
Opt1And k
Opt2Balanced space detailed information and two characteristic indexs of spectral information in the fusion results reach optimum.In other words, according to (4)~(6) formula, carry out objective qualitative evaluation calculating and can obtain optimum weight coefficient k
Opt1Or k
Opt2, it makes the image after merging both reach the highest spatial resolution, has reduced colored distortion simultaneously again to greatest extent;
6, to the low frequency base band coefficient of the wavelet coefficient of the Pixel-level optimum fusion that obtains, and each high frequency wavelet coefficient that the feature level of carrying out merges carries out corresponding wavelet inverse transformation, obtains new strength component I ';
7, I ', H, S are carried out the IHS inverse transformation, the image C after obtaining merging;
Fusion results compares: Fig. 2 (c) is the fusion results (512 * 512) of IHS method, and Fig. 2 (d) is fusion results of the present invention (512 * 512).
Performance evaluation: table 1 when choosing optimum weight coefficient, two index quantification values of the related coefficient of fusion results and average gradient, and contrast with the IHS method.
The performance evaluation index of table 1 fusion results relatively
Wave band | Related coefficient | Average gradient | |||
IHS | R | 0.5624 | 10.6146 | ||
G | 0.4644 | 11.3663 | |||
B | 0.5060 | 9.5875 | |||
The inventive method | k i | k opt1=k 1=0.474;k 2=0.526 | k opt1=k 1=0.586;k 2=0.414 | ||
Related coefficient | Average gradient | Related coefficient | Average gradient | ||
R | 0.8488 | 8.9448 | 0.7950 | 9.0954 | |
G | 0.7691 | 9.4102 | 0.6992 | 9.6055 | |
B | 0.8247 | 7.9353 | 0.7626 | 8.1007 |
Claims (3)
1, a kind of remote sensing image pixel and characteristic binding optimum fusion method is characterized in that comprising following concrete steps:
(1) multispectral image B is carried out the IHS conversion, obtain colourity H, saturation degree S and strength component I respectively, then I component is carried out 3 layers of wavelet decomposition in the IHS color space;
(2) high resolution image A is carried out linear stretch and histogram coupling, carry out wavelet decomposition then, decompose the number of plies and also be 3 layers;
(3) determine one 3 * 3 spatial domain window, respectively the I component of image B and the subband wavelet coefficient of image A are added up, try to achieve average μ (2
J) and variance D (2
J);
(4) the high-frequency sub-band coefficient of corresponding layers of resolution is according to formula
Carry out the feature level and merge, in the formula, 2
JBe the wavelet decomposition number of plies, W
k(2
J, x is 2 y)
JThe high-frequency sub-band coefficient fusion results that obtains under the resolution, W
A k(2
J, x, y) and W
B k(2
J, x y) is respectively the corresponding high-frequency sub-band coefficient of the I component of image A and image B, D
A k, D
B kBe respectively so that (x is the variance of 3 * 3 spatial domain window of center pixel y), and (x y) is the coordinate of center pixel;
(5) to the low frequency base band coefficient of the subband wavelet coefficient of the I component of image B and image A, according to formula
A (2
J, x, y)=k
1A
A(2
J, x, y)+k
2A
B(2
J, x y) carries out optimum Pixel-level and merges the k of weight coefficient
OptAsk excellent according to spectral information evaluation index and spatial resolution evaluation index, in the formula, A
A(2
J, x, y), A
B(2
J, x y) is respectively the correspondence 2 of the I component of image A and image B
JThe low frequency base band data of resolution, k
1, k
2For needs are asked excellent weight coefficient, k
1, k
2Satisfy k
1+ k
2=1, promptly ask excellent Determination of Weight Coefficient to be summed up as the k that satisfies objective function
Opt=k
1=1-k
2
(6) to the low frequency base band coefficient of the wavelet coefficient of the Pixel-level optimum fusion that obtains, and each high frequency wavelet coefficient that the feature level of carrying out merges carries out corresponding wavelet inverse transformation, obtains new strength component I ';
(7) I ', H, S are carried out the IHS inverse transformation, the image C after obtaining merging.
2, as said remote sensing image pixel of claim 1 and characteristic binding optimum fusion method, it is characterized in that weight coefficient k
OptWhen asking excellent, ask excellent objective function according to formula
F(k
opt)=F(k
1,k
2)
=Max(E
SP(k
1,k
2);E
HF(k
1,k
2)) OR Max(E
SP(k
1,k
2);AG(k
1,k
2))
={E
SP(k
opt);E
HF(k
opt)}OR{E
SP(k
opt);AG(k
opt)}
D:g(k
opt):k
opt=k
1=1-k
2(0≤k
opt≤1)
k
opt∈D∈R
Determine; In the formula, E
SP(k
1, k
2) be the spectral information evaluation index, E
HF(k
1, k
2) be the spatial resolution evaluation index, AG (k
1, k
2) be the average gradient evaluation index, D:g (k
Opt) for asking the constraint definition territory of excellent objective function.
3, as said remote sensing image pixel of claim 1 and characteristic binding optimum fusion method, it is characterized in that weight coefficient k
OptWhen asking excellent, utilize formula according to spectral information evaluation and spatial resolution evaluation index
Obtain the curve of spectral information evaluation and spatial resolution evaluation index, intersections of complex curve is the k of optimum weight coefficient
Opt, in the formula, E (i) is an evaluation index, i is weight coefficient k
1The number of times of optimizing.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CNB031290566A CN1303432C (en) | 2003-06-05 | 2003-06-05 | Remote sensing image picture element and characteristic combination optimizing mixing method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CNB031290566A CN1303432C (en) | 2003-06-05 | 2003-06-05 | Remote sensing image picture element and characteristic combination optimizing mixing method |
Publications (2)
Publication Number | Publication Date |
---|---|
CN1472544A CN1472544A (en) | 2004-02-04 |
CN1303432C true CN1303432C (en) | 2007-03-07 |
Family
ID=34153424
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CNB031290566A Expired - Fee Related CN1303432C (en) | 2003-06-05 | 2003-06-05 | Remote sensing image picture element and characteristic combination optimizing mixing method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN1303432C (en) |
Families Citing this family (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1303571C (en) * | 2004-09-02 | 2007-03-07 | 上海交通大学 | Image optimum fusing method based on fuzzy integral |
CN1316431C (en) * | 2004-11-05 | 2007-05-16 | 北京师范大学 | Adjustable remote sensing image fusion method based on wavelet transform |
CN100365435C (en) * | 2005-12-05 | 2008-01-30 | 牛铮 | Analogue technology for imaging spectrograph remote-sensing image in satellite |
CN101604018B (en) * | 2009-07-24 | 2011-09-21 | 中国测绘科学研究院 | Method and system for processing high-definition remote sensing image data |
KR101484971B1 (en) * | 2010-11-24 | 2015-01-21 | 인디언 스터티스티컬 인스티튜트 | Rough wavelet granular space and classification of multispectral remote sensing image |
CN102693551A (en) * | 2011-03-22 | 2012-09-26 | 江苏瑞蚨通软件科技有限公司(中外合资) | Method for realizing three-dimensional reconstruction by multi-spectral image fusion |
CN102254311A (en) * | 2011-06-10 | 2011-11-23 | 中国科学院深圳先进技术研究院 | Method and system for fusing remote sensing images |
CN104156911A (en) * | 2014-07-18 | 2014-11-19 | 苏州阔地网络科技有限公司 | Processing method and system for image fusion |
CN105469364B (en) * | 2015-10-26 | 2018-09-28 | 厦门理工学院 | A kind of Method of Medical Image Fusion of joint wavelet transformed domain and spatial domain |
CN108898569A (en) * | 2018-05-31 | 2018-11-27 | 安徽大学 | A kind of fusion method being directed to visible light and infrared remote sensing image and its fusion results evaluation method |
CN109523497A (en) * | 2018-10-30 | 2019-03-26 | 中国资源卫星应用中心 | A kind of optical remote sensing image fusion method |
CN110706188B (en) * | 2019-09-23 | 2020-10-23 | 北京航天宏图信息技术股份有限公司 | Image fusion method and device, electronic equipment and storage medium |
CN114331936B (en) * | 2021-12-24 | 2024-04-16 | 郑州信大先进技术研究院 | Remote sensing image fusion method based on wavelet decomposition and IHS algorithm improvement |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001084828A1 (en) * | 2000-04-27 | 2001-11-08 | Litton Systems, Inc. | Method and system for fusing images |
CN1402191A (en) * | 2002-09-19 | 2003-03-12 | 上海交通大学 | Multiple focussing image fusion method based on block dividing |
WO2003021967A2 (en) * | 2001-09-04 | 2003-03-13 | Icerobotics Limited | Image fusion systems |
US20030053668A1 (en) * | 2001-08-22 | 2003-03-20 | Hendrik Ditt | Device for processing images, in particular medical images |
-
2003
- 2003-06-05 CN CNB031290566A patent/CN1303432C/en not_active Expired - Fee Related
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001084828A1 (en) * | 2000-04-27 | 2001-11-08 | Litton Systems, Inc. | Method and system for fusing images |
US20030053668A1 (en) * | 2001-08-22 | 2003-03-20 | Hendrik Ditt | Device for processing images, in particular medical images |
WO2003021967A2 (en) * | 2001-09-04 | 2003-03-13 | Icerobotics Limited | Image fusion systems |
CN1402191A (en) * | 2002-09-19 | 2003-03-12 | 上海交通大学 | Multiple focussing image fusion method based on block dividing |
Also Published As
Publication number | Publication date |
---|---|
CN1472544A (en) | 2004-02-04 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN1303432C (en) | Remote sensing image picture element and characteristic combination optimizing mixing method | |
CN100550978C (en) | A kind of self-adapting method for filtering image that keeps the edge | |
CN102063713B (en) | Neighborhood normalized gradient and neighborhood standard deviation-based multi-focus image fusion method | |
US8355586B2 (en) | Decoding apparatus, dequantizing method, distribution determining method, and program thereof | |
CN111612695B (en) | Super-resolution reconstruction method for low-resolution face image | |
Hou et al. | Complex SAR image compression based on directional lifting wavelet transform with high clustering capability | |
CN1897035A (en) | Visible-light and infrared imaging merging method based on Contourlet conversion | |
CN106709891A (en) | Image processing method based on combination of wavelet transform and self-adaptive transform | |
CN104881847A (en) | Match video image enhancement method based on wavelet analysis and pseudo-color processing | |
CN1219079A (en) | Method for compressing digital data | |
CN1282131C (en) | Image merging method based on inseparable wavelet frame | |
Pan et al. | A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT | |
JP2007511941A (en) | Conversion to remove image noise | |
CN1489111A (en) | Remote-sensing image mixing method based on local statistical property and colour space transformation | |
CN101719267B (en) | A kind of denoising noise image and system | |
CN1921562A (en) | Method for image noise reduction based on transforming domain mathematics morphology | |
CN1929552A (en) | Spatial domain pixel data processing method | |
CN1917577A (en) | Method of reducing noise for combined images | |
Li et al. | A SAR image compression algorithm based on Mallat tower-type wavelet decomposition | |
CN1303571C (en) | Image optimum fusing method based on fuzzy integral | |
CN1920881A (en) | Image noise reducing method for Contourlet transform | |
Shi et al. | Remote sensing image compression based on adaptive directional wavelet transform with content-dependent binary tree codec | |
CN1770201A (en) | Adjustable remote sensing image fusion method based on wavelet transform | |
CN1581230A (en) | Remote-senstive image interfusion method based on image local spectrum characteristic | |
CN1741617A (en) | Handle the equipment and the method for the shoot artifacts of picture signal |
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: 20070307 |