CN100511280C - New method for restoring disrepaired image - Google Patents
New method for restoring disrepaired image Download PDFInfo
- Publication number
- CN100511280C CN100511280C CNB2006101125902A CN200610112590A CN100511280C CN 100511280 C CN100511280 C CN 100511280C CN B2006101125902 A CNB2006101125902 A CN B2006101125902A CN 200610112590 A CN200610112590 A CN 200610112590A CN 100511280 C CN100511280 C CN 100511280C
- Authority
- CN
- China
- Prior art keywords
- image
- damaged
- point
- omega
- theta
- 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
Landscapes
- Image Processing (AREA)
Abstract
A digitalized repairing-method of damaged image includes recording image to be repaired as I, using image-dividing means to confirm out damaged region of image, generating a binary image according damaged and undamaged regions in image I, labeling undamaged region in image I as omega, using structure element E to make mathematic form corrosion operation on binary image to obtain corrosion binary image, calculating local variation rate of corrosion image, using structure element E to make expansion operation on region omega and carrying out repairing process according to preset steps.
Description
(1), technical field:
Relate to method, the especially method that the image of local damage can be repaired by digitizing technique that a kind of view data is handled.
(2), background technology:
Image repair (Image Inpainting) is the digitizing recovery technique to archives, ancient painting, film: by take pictures, mode such as scanning, mould/number conversion becomes them after the digital picture, utilizes computing machine reparation again.The standard of repairing is not see the vestige of handling is arranged as far as possible.Restorative procedure master commonly used is pure manual method, and softwares such as digital picture use Photoshop are dealt with, and progressively smears damaged zone, and this processing procedure efficient is very low.Some methods of handling have automatically been studied at present in the world, known two classes that mainly contain, one class is based on the method for partial differential equation, use some smoothness assumption to separate the partial differential equation problem repairing the question resolves itself into, the shortcoming of these class methods is to repair little and elongated zone, and the damaged zone for roomy excessively level and smooth effect can occur after the repairing, leave obvious marks, poor effect.The another kind of synthetic restorative procedure of texture that is based on is searched for a suitable texture automatically in image, it is copied to damaged zone, but the process of this search is quite time-consuming, causes the reparation speed of these class methods very slow.
(3), summary of the invention:
Conventional images restorative procedure speed is slow, efficient is low in order to overcome, and the shortcoming that can't repair enlarged regions, the purpose of this invention is to provide a kind of new image repair method, this method not only can be repaired roomy damaged zone, and fast operation, than approximately fast 100 times of the methods of synthesizing based on texture.
The technical solution adopted for the present invention to solve the technical problems is as follows:
The new method that a kind of disrepaired image through digitization of the present invention is repaired, the step of this method is as follows:
[1], an image to be repaired is designated as I, and by some known image partition methods or man-machine interactively method determine damaged zone in the image.Not damaged and damaged zone according to image I generates a width of cloth bianry image, and wherein the not damaged area relative of image I partly is labeled as Ω.
[2], utilization structure element E does the computing of mathematics morphological erosion to bianry image, the bianry image ε that obtains corroding
E(Ω).
[3], calculate corrosion diagram as ε
EBorder (Ω)
On the localized variation rate of being had a few, method is as follows: establish image I and have n component (I is a gray level image during n=1, and I is the RGB coloured image during n=3), the structure tensor G by following formula computed image I at first,
I in the formula
iI component of presentation video,
Be the gradient of this component, can calculate with known central difference method.Obtain characteristic direction and the eigenwert of tensor G then by following characteristic value decomposition,
θ wherein
+It is bigger eigenvalue
+Pairing proper vector, it can be defined as the localized variation rate of image I at set point.
[4], regional Ω is made dilation operation, obtain regional δ with structural element E
E(Ω).Make d Ω=δ
E(Ω)-Ω represents current zone to be repaired, then for arbitrfary point x
*∈ d Ω, repair (referring to Fig. 1) according to following steps:
(4.1), seek
Go up and x
*The point that distance is enough approaching is promptly sought the set of point
Wherein r is a predefined search radius.The value of r will be considered the compromise of search speed and search precision.
(4.2), use following formula set of computations N (x
*) in an optimum x
0:
Wherein α be one greater than zero parameter.The connotation of this formula is to guarantee at an x
0The straight line x of place
*-x
0With characteristic direction θ
+(x
0) between near vertical, guarantee eigenvalue simultaneously
+(x
0) be maximum.
(4.3), defining point x
m=(x
*+ x
0)/2 use following formula to repair breaking point x
*The pixel value at place:
‖ Δ I ‖=‖ I (x wherein
m)-I (x
0) ‖, Ω still represents unbroken image-region, ε is a positive parameter.
[5], make ε
E(Ω)=and Ω, Ω=δ
E(Ω), and returned for the 3rd step, all breaking points are repaired and finish in image I.
Related symbol connotation is concluded and is described as follows in the above-mentioned steps:
The image that I is to be repaired
Unbroken zone in the Ω image I
The structural element of E mathematical morphology
ε
E(Ω) regional Ω is made the result of erosion operation by structural element E
Zone ε
EBorder (Ω)
The structure tensor of G image I
I
iI the component of presentation video I
λ
+, two eigenwerts of λ _ structure tensor G, λ
+〉=λ _
θ
+, θ _ eigenvalue
+And λ _ pairing proper vector
δ
E(Ω) regional Ω is made the result of dilation operation by structural element E
Zone to be repaired, i.e. δ in the d Ω current iteration
E(Ω)-Ω
x
*Point to be repaired among the d Ω of zone
N (x
*) border
Go up and some x
*Enough set of approaching point of distance are defined by formula (1)
The predefined search radius of r
x
0Set N (x
*) in an optimum, define by formula (2)
α is greater than zero parameter
x
mPoint x
*With an x
0Mid point, i.e. (x
*+ x
0)/2
‖ Δ I ‖ pixel value I (x
m) and I (x
0) the norm of difference, i.e. ‖ I (x
m)-I (x
0) ‖
One of ε is on the occasion of parameter
Technique effect of the present invention is as follows:
View data for breakage provides effective digitizing restorative procedure, can repair roomy damaged zone (as Fig. 3, Fig. 4, Fig. 5 and shown in Figure 6), overcome the shortcoming that the existing image repair method method of partial differential equation (particularly based on) can only be repaired the damaged zone of " fine rule " shape.Method remediation efficiency height of the present invention, speed is fast, than general fast two orders of magnitude of the method for synthesizing based on texture.
(4), description of drawings:
Fig. 1 the present invention carries out the schematic diagram of image repair.
The software flow pattern of Fig. 2 a specific embodiment of the present invention.
Fig. 3 carries out the example of image repair with method of the present invention.
Fig. 4 carries out the example of image repair with method of the present invention.
Fig. 5 carries out the example of image repair with method of the present invention.
Fig. 6 carries out the example of image repair with method of the present invention.
(5) embodiment:
A specific embodiment of the present invention is as follows:
Consulting shown in Figure 1ly, is the schematic diagram that the present invention carries out image repair.Pixel in each grid representative image among the figure, the zone that the region representation of dotted line grid is to be repaired, wherein the grid of grey is represented the breaking point x that current needs are repaired
*The not damaged regional Ω of the region representation of solid line grid.Part adjacent with the area to be repaired among the Ω is called the border
Set N (the x of the light gray areas representative point among the Ω
*).
Consult the process flow diagram of Fig. 2, at first import an image I to be repaired, according to the not damaged regional Ω and the damaged zone generation bianry image of image I.
In second step, the square structure element E that uses 3 * 3 makes erosion operation to regional Ω, the bianry image ε that obtains corroding
E(Ω).
In the 3rd step, utilize aforementioned techniques computation schemes border
On the localized variation rate θ that had a few
+
The 4th step, with structural element E regional Ω is made dilation operation, obtain δ
E(Ω).Make d Ω=δ
E(Ω)-and Ω, for the some x that does not repair among the d Ω
*, repair with the following methods:
(a) according to the set N (x of formula (1) calculation level
*), wherein get parameter
(b) according to formula (2) set of computations N (x
*) in an optimum x
0, wherein get parameter alpha=1;
(c) repair some x according to formula (3)
*Pixel value I (the x at place
*), wherein get parameter ε=5.
In the 5th step, make ε
E(Ω)=and Ω, Ω=δ
E(Ω), check whether Ω comprises the entire image zone.If then repair and finish; Otherwise returning for the 3rd step continues to repair.
Seeing also shown in Fig. 3,4,5,6, is the example that method of the present invention is carried out image repair.The left figure of Fig. 3 is damaged image, and the centre is the result who repairs with Partial Differential Equation method, and right figure is the result who repairs with method of the present invention.Fig. 4 erases a bunch of flowers among the left figure automatically with method of the present invention.Fig. 5 is with the damaged zone in the method repairing retina image of the present invention.Fig. 6 erases arm and the microphone of speaker among the left figure automatically with method of the present invention.
Claims (1)
1, a kind of new method of disrepaired image through digitization reparation, it is characterized in that: this method step is as follows:
[1], an image to be repaired is designated as I, and by image partition method or man-machine interactively method determine damaged zone in the image; Not damaged and damaged zone according to image I generates a width of cloth bianry image, and wherein the not damaged area relative of image I partly is labeled as Ω;
[2], utilization structure element E does the computing of mathematics morphological erosion to bianry image, the bianry image ε that obtains corroding
E(Ω);
[3], calculate corrosion diagram as ε
EBorder (Ω)
On the localized variation rate of being had a few, method is as follows: establish image I and have n component, the structure tensor G by following formula computed image I at first,
I in the formula
iI component of presentation video,
Be the gradient of this component, calculate, obtain characteristic direction and the eigenwert of tensor G then by following characteristic value decomposition with known central difference method,
θ wherein
+It is bigger eigenvalue
+Pairing proper vector, it is defined as the localized variation rate of image I at set point;
[4], regional Ω is made dilation operation, obtain regional δ with structural element E
E(Ω); Make d Ω=δ
E(Ω)-Ω represents current zone to be repaired, then for arbitrfary point x
*∈ d Ω, repair according to following steps:
(4.1), seek
Go up and x
*The point that distance is enough approaching is promptly sought the set of point
Wherein r is a predefined search radius, and the value of r will be considered the compromise of search speed and search precision;
(4.2), use following formula set of computations N (x
*) in an optimum x
0:
Wherein α be one greater than zero parameter; The connotation of this formula is to guarantee at an x
0The straight line x of z place
*-x
0With characteristic direction θ
+(x
0) between near vertical, guarantee eigenvalue simultaneously
+(x
0) be maximum;
(4.3), defining point x
m=(x
*+ x
0)/2 use following formula to repair breaking point x
*The pixel value at place:
‖ Δ I ‖=‖ I (x wherein
m)-I (x
0) ‖, Ω still represents unbroken image-region, ε is a positive parameter;
[5], make ε
E(Ω)=and Ω, Ω=δ
E(Ω), and returned for [3] step, all breaking points are repaired and finish in image I.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CNB2006101125902A CN100511280C (en) | 2006-08-24 | 2006-08-24 | New method for restoring disrepaired image |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CNB2006101125902A CN100511280C (en) | 2006-08-24 | 2006-08-24 | New method for restoring disrepaired image |
Publications (2)
Publication Number | Publication Date |
---|---|
CN101093579A CN101093579A (en) | 2007-12-26 |
CN100511280C true CN100511280C (en) | 2009-07-08 |
Family
ID=38991821
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CNB2006101125902A Expired - Fee Related CN100511280C (en) | 2006-08-24 | 2006-08-24 | New method for restoring disrepaired image |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN100511280C (en) |
Families Citing this family (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101510303B (en) * | 2009-03-26 | 2011-09-14 | 北京兆维电子(集团)有限责任公司 | Method and system for image renovation |
CN103093426B (en) * | 2012-12-14 | 2015-05-27 | 西安电子科技大学 | Method recovering texture and illumination of calibration plate sheltered area |
CN103679664B (en) * | 2013-12-30 | 2016-07-06 | 北京航空航天大学 | A kind of Enhancement Method that can retain image detail utilizing mathematical morphology alternative filter |
CN104657953A (en) * | 2015-03-04 | 2015-05-27 | 中山大学 | Famous painting repairing method based on image processing technology |
CN111369452A (en) * | 2020-02-26 | 2020-07-03 | 青海民族大学 | Large-area image local damage point optimization extraction method |
-
2006
- 2006-08-24 CN CNB2006101125902A patent/CN100511280C/en not_active Expired - Fee Related
Non-Patent Citations (6)
Title |
---|
An image inpainting technique based on the fast marchingmethod. Alexandru Telea.Journal of Graphics,Vol.9 No.1. 2004 * |
image inpainting. M. Bertalmío, G. Sapiro, V. Caselles and C. Ballester.Proceedings of SIGGRAPH 2000, New Orleans, USA. 2000 * |
image inpainting. M. Bertalmío, G. Sapiro,V. Caselles and C. Ballester.Proceedings of SIGGRAPH 2000, New Orleans, USA. 2000 * |
Navier-stokes, fluid dynamics, and image and video inpainting. Bertalmio, M. Bertozzi, A.L. Sapiro, G.Computer Vision and Pattern Recognition, 2001. CVPR 2001,Vol.1 . 2001 * |
Navier-stokes, fluid dynamics, and image and video inpainting. Bertalmio, M. Bertozzi, A.L. Sapiro, G.Computer Vision and Pattern Recognition, 2001. CVPR 2001,Vol.1. 2001 * |
基于水平线插值的图像修复算法. 顾建平,韩华,彭思龙.计算机工程,第32卷第9期. 2006 * |
Also Published As
Publication number | Publication date |
---|---|
CN101093579A (en) | 2007-12-26 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN100511280C (en) | New method for restoring disrepaired image | |
CN101661613B (en) | Image restoration method based on image segmentation, and system therefor | |
CN100583158C (en) | Cartoon animation fabrication method based on video extracting and reusing | |
CN111982910B (en) | Weak supervision machine vision detection method and system based on artificial defect simulation | |
CN102999887B (en) | Sample based image repairing method | |
CN103377462B (en) | The method and apparatus that scan image is processed | |
CN103886561B (en) | Criminisi image inpainting method based on mathematical morphology | |
CN101571950A (en) | Image restoring method based on isotropic diffusion and sparse representation | |
CN111402209A (en) | U-Net-based high-speed railway steel rail damage detection method | |
CN110443791B (en) | Workpiece detection method and device based on deep learning network | |
CN101833668B (en) | Detection method for similar units based on profile zone image | |
CN108647574A (en) | Floating material image detection model generating method, recognition methods and equipment | |
Shao et al. | From IC layout to die photograph: a CNN-based data-driven approach | |
Cline et al. | Computer-aided surface reconstruction of interference contours | |
CN103077516A (en) | Digital rubbing method for stone inscription characters | |
KR102600475B1 (en) | Deep learning-based data augmentation method for product defect detection learning | |
CN113706464A (en) | Printed matter appearance quality detection method and system | |
CN109598782A (en) | Building historical relic restorative procedure, storage medium based on dimensional Modeling Technology | |
CN107944451A (en) | The row cutting method and system of a kind of ancient Tibetan books document | |
CN114399505B (en) | Detection method and detection device in industrial detection | |
Wei et al. | GeoDualCNN: Geometry-supporting dual convolutional neural network for noisy point clouds | |
CN116205876A (en) | Unsupervised notebook appearance defect detection method based on multi-scale standardized flow | |
Zou et al. | Automatic segmentation, inpainting, and classification of defective patterns on ancient architecture using multiple deep learning algorithms | |
CN113240790B (en) | Rail defect image generation method based on 3D model and point cloud processing | |
CN115270184A (en) | Video desensitization method, vehicle video desensitization method and vehicle-mounted processing system |
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: 20090708 Termination date: 20130824 |