CN105528772A - Image fusion method based on guidance filtering - Google Patents
Image fusion method based on guidance filtering Download PDFInfo
- Publication number
- CN105528772A CN105528772A CN201510880750.7A CN201510880750A CN105528772A CN 105528772 A CN105528772 A CN 105528772A CN 201510880750 A CN201510880750 A CN 201510880750A CN 105528772 A CN105528772 A CN 105528772A
- Authority
- CN
- China
- Prior art keywords
- image
- mean
- max
- filtering
- carry out
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000001914 filtration Methods 0.000 title claims abstract description 31
- 238000007500 overflow downdraw method Methods 0.000 title abstract description 5
- 238000000034 method Methods 0.000 claims abstract description 21
- 238000004364 calculation method Methods 0.000 claims abstract description 3
- 230000004927 fusion Effects 0.000 abstract description 7
- 230000000694 effects Effects 0.000 abstract description 4
- 230000000007 visual effect Effects 0.000 description 4
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000010304 firing Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/10—Image enhancement or restoration using non-spatial domain filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20221—Image fusion; Image merging
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
Abstract
The present invention relates to an image fusion method based on guidance filtering. The method comprises the steps of (1) carrying out maximum value filtering on an original infrared image I<i> to obtain an image I<i><max>, (2) using a guidance filtering algorithm to carry out further filtering processing on the I<i><max> and output an image I<i><GF>, (3) directly overlaying the image I<i><GF> to an original visible light image I<v> and obtaining an image I<f>, (4) carrying out gamma correction on the image I<f> and obtaining a final fusion image. The method is based on pixel fusion, the gray level of only a part of the region is raised, a fusion effect 'unnatural' phenomenon is avoided, in addition the algorithm calculation complexity is low, and the real-time processing is easy.
Description
Technical field
The invention belongs to technical field of image processing, be specifically related to a kind of image interfusion method based on guiding filtering.
Background technology
In view of visible images resolution is higher, the detailed information such as edge, texture is abundanter, but is easily subject to the impact of the external environments such as weather; Infrared image adaptive faculty is comparatively strong, can penetrate general smog, can continuous firing round the clock, but gradation of image is determined by temperature, and details is less, and the sense of reality is strong, therefore both is mutually merged and is supplemented, can reach good visual effect.At present, relevant Image Fusion has following two kinds: 1. pixel method of weighted mean: interested image-region is composed larger weights to obtain advantage display.The advantage of the method is that computation complexity is low, is easy to real-time process, and shortcoming is that weighted criterion is difficult to determine, causes syncretizing effect otherness very large.2. multi-Resolution Image Fusion method: by picture breakdown on different frequency bands, then merges each frequency range data according to certain fusion rule, finally the reconstruct of each frequency range is obtained fused image.These class methods typically have: laplacian pyramid method, Wavelet Transform etc.Advantage is to select different fusion rules according to image different frequency range characteristic, specific aim is stronger, and shortcoming lacks relevance between different frequency range pixel, causes merging reconstructed image too " stiff ", visual effect is not good, and computation complexity is also higher in addition.
Summary of the invention
The invention provides a kind of image interfusion method based on guiding filtering, be intended to solve existing visible ray and infrared image fusion method computation complexity is high, merge the not good problem of visual effect.
Image interfusion method of the present invention comprises the steps:
1) to original infrared image I
icarry out maximal value filtering, filter result
can be expressed as:
Wherein, Ω
rx () represents that centre coordinate is x, radius is the image-region of R;
2) guiding filtering algorithm pair is adopted
do further filtering process, output image
Wherein, GuidedFilter () represents guiding filtering algorithm, and r represents filter radius, and ε represents regularization parameter;
3) by image
directly be superimposed upon primary visible light image I
von, obtain image I
f:
4) to image I
fcarry out Gamma correction, obtain final fused images I '
f:
Wherein, γ represents correction coefficient, and p represents the figure place of view data.
Described guiding filtering algorithm comprises the steps:
1) to original infrared image I
iand
carry out mean filter, obtain average
and try to achieve original infrared image I
iautocorrelation value
and and image
cross correlation value
namely
2) according to average
autocorrelation value
with cross correlation value
and average
try to achieve variance
and covariance
namely
3) then according to step 2) to result of calculation carry out following computing, finally obtain merging rear output image
mean
a=f
mean(a),mean
b=f
mean(b),
The invention has the beneficial effects as follows: the present invention fully takes into account visible images and infrared image advantage separately, namely visible images details is enriched, and infrared image can to the highlighted display of some interested targets.All retain visible light part when doing image co-registration, infrared part then instruction filtering extracts highlight information for this reason.Merge owing to the present invention is based on Pixel-level, and just subregion improves grey level, can't occur the phenomenon of syncretizing effect " not nature ", algorithm computation complexity is lower in addition, is easy to real-time process.
Accompanying drawing explanation
Fig. 1 is the fusion method FB(flow block) of the present embodiment;
Fig. 2 is the design sketch of the present embodiment, and wherein, (a) figure is primary visible light image, and (b) figure is original infrared image, and (c) figure is result after merging.
Embodiment
Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail.
The image interfusion method based on guiding filtering in the present embodiment, first carries out maximal value filtering acquisition highlight information wherein to infrared image; Then use original infrared image to do guiding filtering (GuidedFilter) process as guide image (GuidanceImage) to highlighted part, result is directly superimposed upon can by light image; Finally superimposed image is carried out Gamma correction (GammaCorrection) and namely finally merged output to adjust pixel grayscale, concrete steps are as follows:
1) to original infrared image I
icarry out maximal value filtering, filter result
can be expressed as:
Wherein, Ω
rx () represents that centre coordinate is x, radius is the image-region of R;
2) guiding filtering algorithm pair is adopted
do further filtering process, output image
Wherein, GuidedFilter () represents guiding filtering algorithm, and r represents filter radius, and ε represents regularization parameter;
3) by image
directly be superimposed upon primary visible light image I
von, obtain image I
f:
4) to image I
fcarry out Gamma correction, obtain final fused images I '
f:
Wherein, γ represents correction coefficient, and p represents the figure place of view data.
Directiveness filtering algorithm implementation procedure is as follows:
Wherein, f
meanrepresent that filter radius is the mean filter of r, mean, corr, var and cov represent average, correlation, variance and covariance respectively.
In the present embodiment, visible ray used and infrared image are 8, the gray level image of 360 × 270, as shown in (a) figure in accompanying drawing 2 He (b) figure.
According to above-mentioned implementation step, (1) formula is first utilized to do maximal value filtering process to original infrared image, wherein radius R=7; Then (2) formula is utilized to try to achieve guiding filter result, wherein filter radius r=30, regularization parameter ε=10
-3; Finally, use (3) formula by filtering image and primary visible light imaging importing, stack result is passed through again (4), and namely formula Gamma correction obtains final fusion output, wherein correction coefficient γ=0.8, image figure place n=8.
(c) figure in accompanying drawing 2 is final syncretizing effect, compares with infrared image with primary visible light, has both remained the detailed information of visible images, again highlighted for the interesting target in infrared image display, and has good visual effect.
Claims (2)
1. based on an image interfusion method for guiding filtering, it is characterized in that, comprise the steps:
1) to original infrared image I
icarry out maximal value filtering, filter result
can be expressed as:
Wherein, Ω
rx () represents that centre coordinate is x, radius is the image-region of R;
2) guiding filtering algorithm pair is adopted
do further filtering process, output image
Wherein, GuidedFilter () represents guiding filtering algorithm, and r represents filter radius, and ε represents regularization parameter;
3) by image
directly be superimposed upon primary visible light image I
von, obtain image I
f:
4) to image I
fcarry out Gamma correction, obtain final fused images I '
f:
Wherein, γ represents correction coefficient, and p represents the figure place of view data.
2. according to claim 1 based on the image interfusion method of guiding filtering, it is characterized in that, described guiding filtering algorithm comprises the steps:
1) to original infrared image I
iand
carry out mean filter, obtain average
and try to achieve original infrared image I
iautocorrelation value
and and image
cross correlation value
namely
2) according to average
autocorrelation value
with cross correlation value
and average
try to achieve variance
and covariance
namely
3) then according to step 2) to result of calculation carry out following computing, finally obtain merging rear output image
mean
a=f
mean(a),mean
b=f
mean(b),
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510880750.7A CN105528772B (en) | 2015-12-05 | 2015-12-05 | A kind of image interfusion method based on directiveness filtering |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510880750.7A CN105528772B (en) | 2015-12-05 | 2015-12-05 | A kind of image interfusion method based on directiveness filtering |
Publications (2)
Publication Number | Publication Date |
---|---|
CN105528772A true CN105528772A (en) | 2016-04-27 |
CN105528772B CN105528772B (en) | 2018-10-16 |
Family
ID=55770977
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510880750.7A Active CN105528772B (en) | 2015-12-05 | 2015-12-05 | A kind of image interfusion method based on directiveness filtering |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN105528772B (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107424179A (en) * | 2017-04-18 | 2017-12-01 | 微鲸科技有限公司 | A kind of image equalization method and device |
CN109146904A (en) * | 2018-08-13 | 2019-01-04 | 合肥英睿***技术有限公司 | The method and apparatus of infrared image object profile is shown in visible images |
CN109886904A (en) * | 2019-01-25 | 2019-06-14 | 北京市遥感信息研究所 | A kind of SAR image and low resolution Multispectral Image Fusion Methods and system |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1822046A (en) * | 2006-03-30 | 2006-08-23 | 上海电力学院 | Infrared and visible light image fusion method based on regional property fuzzy |
CN101873440A (en) * | 2010-05-14 | 2010-10-27 | 西安电子科技大学 | Infrared and visible light video image fusion method based on Surfacelet conversion |
CN102789640A (en) * | 2012-07-16 | 2012-11-21 | 中国科学院自动化研究所 | Method for fusing visible light full-color image and infrared remote sensing image |
CN102982518A (en) * | 2012-11-06 | 2013-03-20 | 扬州万方电子技术有限责任公司 | Fusion method of infrared image and visible light dynamic image and fusion device of infrared image and visible light dynamic image |
US8447137B2 (en) * | 2011-04-12 | 2013-05-21 | Csi Ricerca & Ambiente Srl | Method of image fusion |
WO2015157058A1 (en) * | 2014-04-07 | 2015-10-15 | Bae Systems Information & Electronic Systems Integration Inc. | Contrast based image fusion |
-
2015
- 2015-12-05 CN CN201510880750.7A patent/CN105528772B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1822046A (en) * | 2006-03-30 | 2006-08-23 | 上海电力学院 | Infrared and visible light image fusion method based on regional property fuzzy |
CN101873440A (en) * | 2010-05-14 | 2010-10-27 | 西安电子科技大学 | Infrared and visible light video image fusion method based on Surfacelet conversion |
US8447137B2 (en) * | 2011-04-12 | 2013-05-21 | Csi Ricerca & Ambiente Srl | Method of image fusion |
CN102789640A (en) * | 2012-07-16 | 2012-11-21 | 中国科学院自动化研究所 | Method for fusing visible light full-color image and infrared remote sensing image |
CN102982518A (en) * | 2012-11-06 | 2013-03-20 | 扬州万方电子技术有限责任公司 | Fusion method of infrared image and visible light dynamic image and fusion device of infrared image and visible light dynamic image |
WO2015157058A1 (en) * | 2014-04-07 | 2015-10-15 | Bae Systems Information & Electronic Systems Integration Inc. | Contrast based image fusion |
Non-Patent Citations (1)
Title |
---|
SHUTAO LI 等: "Image Fusion with Guided Filtering", 《IEEE TRANSACTIONS ON IMAGE PROCESSING》 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107424179A (en) * | 2017-04-18 | 2017-12-01 | 微鲸科技有限公司 | A kind of image equalization method and device |
CN109146904A (en) * | 2018-08-13 | 2019-01-04 | 合肥英睿***技术有限公司 | The method and apparatus of infrared image object profile is shown in visible images |
CN109886904A (en) * | 2019-01-25 | 2019-06-14 | 北京市遥感信息研究所 | A kind of SAR image and low resolution Multispectral Image Fusion Methods and system |
Also Published As
Publication number | Publication date |
---|---|
CN105528772B (en) | 2018-10-16 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN108830796B (en) | Hyperspectral image super-resolution reconstruction method based on spectral-spatial combination and gradient domain loss | |
Fang et al. | A variational approach for pan-sharpening | |
CN107154023B (en) | Based on the face super-resolution reconstruction method for generating confrontation network and sub-pix convolution | |
Wang et al. | Dehazing for images with large sky region | |
CN106408524B (en) | Depth image enhancement method based on two-dimensional image assistance | |
CN102006425B (en) | Method for splicing video in real time based on multiple cameras | |
CN110570353A (en) | Dense connection generation countermeasure network single image super-resolution reconstruction method | |
CN101872472B (en) | Method for super-resolution reconstruction of facial image on basis of sample learning | |
CN103020918B (en) | Shape-adaptive neighborhood mean value based non-local mean value denoising method | |
Anil et al. | A novel approach using active contour model for semi-automatic road extraction from high resolution satellite imagery | |
CN102903098A (en) | Depth estimation method based on image definition difference | |
CN104537678B (en) | A kind of method that cloud and mist is removed in the remote sensing images from single width | |
CN104008539A (en) | Image super-resolution rebuilding method based on multiscale geometric analysis | |
CN107610093B (en) | Full-reference image quality evaluation method based on similarity feature fusion | |
CN104574328A (en) | Color image enhancement method based on histogram segmentation | |
CN101976436A (en) | Pixel-level multi-focus image fusion method based on correction of differential image | |
CN104504676A (en) | Full-reference image quality evaluation method based on multi-vision sensitive feature similarity | |
CN102096913B (en) | Multi-strategy image fusion method under compressed sensing framework | |
CN104268833A (en) | New image fusion method based on shift invariance shearlet transformation | |
CN103020898A (en) | Sequence iris image super-resolution reconstruction method | |
CN114612359A (en) | Visible light and infrared image fusion method based on feature extraction | |
CN104036481A (en) | Multi-focus image fusion method based on depth information extraction | |
CN103971354A (en) | Method for reconstructing low-resolution infrared image into high-resolution infrared image | |
Xiao et al. | Image Fusion | |
CN107169946A (en) | Image interfusion method based on non-negative sparse matrix Yu hypersphere color transformation |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |