CN101996393A - Super-resolution method based on reconstruction - Google Patents

Super-resolution method based on reconstruction Download PDF

Info

Publication number
CN101996393A
CN101996393A CN2009100563072A CN200910056307A CN101996393A CN 101996393 A CN101996393 A CN 101996393A CN 2009100563072 A CN2009100563072 A CN 2009100563072A CN 200910056307 A CN200910056307 A CN 200910056307A CN 101996393 A CN101996393 A CN 101996393A
Authority
CN
China
Prior art keywords
pixel
boundary
resolution
pixels
super
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
CN2009100563072A
Other languages
Chinese (zh)
Other versions
CN101996393B (en
Inventor
鲁道夫
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.)
Fudan University
Original Assignee
Fudan 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 Fudan University filed Critical Fudan University
Priority to CN2009100563072A priority Critical patent/CN101996393B/en
Publication of CN101996393A publication Critical patent/CN101996393A/en
Application granted granted Critical
Publication of CN101996393B publication Critical patent/CN101996393B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Image Processing (AREA)

Abstract

The invention belongs to the technical field of computer multimedia technology and digital image processing, which relates to a super-resolution method based on reconstruction. On the basis of assumption of a boundary linear model, a low-resolution image is regarded as a combination of objects with polygon boundaries; a boundary segment expression of an original object is extracted from the low-resolution image; accordingly, in a high-resolution image, pixels on the boundaries are accurately colored; and other pixels not on the boundaries are colored according to a traditional method. Compared with traditional popular quick treating methods PR and LI, the super-resolution method of the invention can be used for obtaining a more accurate boundary so that the imaging effect is better and the image is clearer to the largest degree.

Description

A kind of based on the super-resolution method of rebuilding
Technical field
The invention belongs to Computer Multimedia Technology and digital image processing techniques field, relate to a kind of based on the super-resolution method of rebuilding.
Background technology
Super-resolution technique is meant, generates the technology of high-definition picture from the low-resolution image of input.The super-resolution technique applied scene is quite extensive, and for example, we need show same width of cloth image on the display of different resolution, televisor, mobile phone; And for example, we wish to improve the photographic quality that a pair is taken by low-resolution camera; Perhaps we wish to improve the resolution characteristic of image identification system by improving resolution; Also can upgrade to HDTV form image more clearly to the image of DVD form.
The notion of super-resolution technique is put forward by Tsai and Huang.Most basic super-resolution method comprises Pixel replication (PR).This method only zooms into single pixel the pixel of the same race of a square formation.Generally, the effect of PR and bad.Most super-resolution methods have all used space interpolation (Spatial interpolation).Wherein, linear interpolation Linear interpolation (LI) method is the most common.This method is set at a cum rights with the pixel of centre and heavily is the average of 4 pixel distances of this position and near adjacency.List of references related to the present invention has:
1.T.Acharya?and?P.-S.Tsai.Computational?foundations?of?image?interpolation?algorithms.ACMUbiquity,8,2007.
2.C.B.Atkins,C.A.Bouman,and?J.P.Allebach.Optimal?image?scaling?using?pixel?classification.In?Proceedings?of?the?2001?International?Conference?on?Image?Processing(ICIP’01),volume?3,pages?864-867,2001.
3.S.Baker?and?T.Kanade.Limits?on?super-resolution?and?how?to?break?them.In?Proceedings?of?the2000?IEEE?Conference?on?Computer?Vision?and?Pattern?Recognition(CPVR’00),volume?2,pages372-379,2000.
4.P.Blomgren,G.Papanicolaou,and?H.Zhao.Super-resolution?in?time?reversal?acoustics.Journalof?the?Acoustical?Society?of?America,111:230-248,2002.
5.T.Blu,P.Th′evenaz,and?M.Unser.Generalized?interpolation:higher?quality?at?no?additionalcost.In?Proceedings?of?the?1999International?Conference?on?Image?Processing(ICIP’99),volume?3,pages?667-671,1999.
6.F.M.Candocia?and?J.C.Principe.Superresolution?of?images?with?learned?multiplereconstruction?kernels.In?L.Guan,S.Y.Kung,and?J.Larsen,editors,Multimedia?Image?and?VideoProcessing,chapter?4,pages?219-243.CRC?Press,New?York,2000.
7.A.Corduneanu?and?J.C.Platt.Learning?spatially-variable?filters?for?superresolution?of?text.InProceedings?of?the?2010?International?Conference?on?Image?Processing(ICIP’10),volume?1,pages849-852,2005.
8.L.Davis.A?survey?of?edge?detection?techniques.Computer?Graphics?and?Image?Processing,4:248-270,1975.
9.T.K.Dey,K.Mehlhorn,and?E.A.Ramos.Curve?reconstruction:connecting?dots?with?goodreason.Computational?Geometry:Theory?and?Applications,10:289-303,2000.
10.S.Farsiu,M.Elad,and?P.Milanfar.A?practical?approach?to?super-resolution.In?Proceedings?ofthe?40th?Asilomar?Conference?on?Signals,Systems?and?Computers,2006.Invited?paper.
11.W.T.Freeman,T.R.Jones,and?E.C.Pasztor.Example-based?super-resolution.IEEE?ComputerGraphics?and?Applications,pages?56-65,2002.
12.T.L.Friedman.The?World?is?Flat:A?Brief?Historyof?the?Twenty-First?Century.Farrar,Straussand?Giroux,2005.
13.M.Irani?and?S.Peleg.Super?resolution?from?image?sequences.In?Proceedings?of?the?10thInternational?Conference?on?Pattern?Recognition(ICPR’90),volume?2,pages?115-120,1990.
14.Z.Jiang,T.-T.Wong,and?H.Bao.Practical?super-resolution?from?dynamic?video?sequences.InProceedings?of?the?2003?IEEE?Conference?on?Computation?Vision?and?Pattern?Recognition(CVPR’03),volume?2,pages?549-554,2003.
15.T.M.Lehmann,C.G¨onner,and?K.Spitzer.Survey:interpolation?methods?in?medical?imageprocessing.IEEE?Transactions?on?Medical?Imaging,18(11):1049-1075,1999.
16.T.Lengauer?and?K.Mehlhorn.The?HILL?system:a?design?environment?for?the?hierarchicalspecification,compaction,and?simulation?of?integrated?circuit?layouts.In?Jr.Paul?Penfield,editor,Proceedings?of?the?MIT?VLSI?Conference.Artech?House,Inc.,1984.
17.X.Li?and?M.T.Orchard.New?edge-directed?interpolation.IEEE?Transactions?on?ImageProcessing,10(10):1521-1527,2001.
18.Z.Lin?and?H.-Y.Shum.Fundamental?limits?of?reconstruction-based?superresolution?algorithmsunder?local?translation.IEEE?Transactions?on?Pattern?Analysis?and?Machine?Intelligence,26(1):83-97,2004.
19.K.Mehlhorn.On?the?size?of?sets?of?computable?functions.In?In?Proceedings?of?the?14th?IEEESymposium?on?Automata?and?Switching?Theory,pages?190-196,1973.
20.K.Mehlhorn?and?S.N¨aher.The?LEDA?Platform?for?Combinatorial?and?Geometric?Computing.Cambridge?University?Press,Cambridge,England,1999.
21.E.Meijering.A?chronology?of?interpolation:from?ancient?astronomy?to?modern?signal?andimage?processing.Proceedings?of?the?IEEE,90(3):319-342,2002.
22.B.Mitra.Gaussian-based?edge-detection?methods:a?survey.IEEE?Transactions?on?Systems,Man,and?Cybernetics-Part?C:Applications?and?Reviews,32(3),2002.
Summary of the invention
The present invention aims to provide a kind of based on the super-resolution method of rebuilding, and extracts the boundary sections expression formula of original objects from low-resolution image, then borderline pixel is carried out accurately painted, thereby obtain high-definition picture.
Technical scheme of the present invention is: a kind of based on the super-resolution method of rebuilding, it comprises the steps:
Step 1: in given low-resolution image, at first seek the pixel of those existence, duplicate the PR method with pixel then and handle these pixels, obtain the gray-scale value C of each newly-generated corresponding high-resolution pixel in abutting connection with same color;
Step 2: distinguish boundary pixel and gradual change pixel, boundary pixel is calculated the expression formula of the boundary sections of passing their pixels; The gradual change pixel is gone to step 5 to be handled;
Step 3: the boundary line segment table is reached formula carry out approximate processing, make the intersection point of adjacent boundary line segment overlap, guarantee to couple together that the border that forms is continuous, smooth; If the boundary sections intersection point on the common edge does not overlap, then with the mid point of two intersection points as the border intersection point of two boundary pixels on this limit, go to step 2, recomputate the boundary line expression formula of this position;
Step 4: utilize the boundary line expression formula of gained, and the gray-scale value of both sides, border object, directly calculate the gray-scale value of respective pixel in the high-definition picture;
Step 5: all remaining pixels are labeled as the gradual change pixel, handle with linear interpolation LI method.
Wherein described differentiation boundary pixel of step 2 and gradual change pixel, the method step that boundary pixel is calculated the expression formula of passing their pixel boundary line segments is as follows:
Step 21: the approximate match in the boundary line of two different pixels is become straight-line segment with the surface level oblique, and the left and right sides of described straight-line segment is the color of two different pixels, and its gray-scale value is respectively C lAnd C rFor the boundary line of level, can be clockwise or be rotated counterclockwise 90 ° with described image;
Step 22: get the unit grid square for described straight-line segment, the length of side of each grid is 0.5, makes 3 abscissa value that straight-line segment and adjacent two grids intersect be respectively a, b and c;
Step 23: set up two grey scale pixel value C that straight-line segment passes through adjacent two along slope coordinate lattice bAnd C tFormula;
Step 24: utilize the geometric relationship of a, b and c, try to achieve a, b and c value; If 0<a, b, c<1, then described two different pixels are boundary pixel, thereby obtain the formula that embodies of boundary straight line section; Otherwise described two different pixels are the gradual change pixel.
The super-resolution method that the present invention is based on reconstruction has some application, such as the resolution of the photo that can use super-resolution technique raising experience recognition of face instrument to shoot, thereby improves the success ratio of recognition of face; And for example can adopt this method to improve the resolution that mobile phone is taken pictures, make photograph more clear; And for example can the DVD image be promoted with super-resolution technique is the image of HDTV.
Compare with existing popular immediate processing method PR and LI, super-resolution method of the present invention can obtain border more accurately, makes imaging effect better, can make to a great extent that image becomes more clear.
Description of drawings
Fig. 1 is the FB(flow block) of super-resolution method of the present invention.
Fig. 2 is the border expression formula computing method synoptic diagram of super-resolution method of the present invention.
Fig. 3 (a) is an original low-resolution image;
Fig. 3 (b) is the image after super-resolution method of the present invention is handled;
Fig. 3 (c) is the image after the PR algorithm process;
Fig. 3 (d) is the image after the LI method is handled.
Fig. 4 (a) is original olympic logo pattern;
Fig. 4 (b) is the high resolving power olympic logo pattern after super-resolution method of the present invention is handled;
Fig. 4 (c) is the emblem partial enlarged drawing of Fig. 4 (a);
Fig. 4 (d) is the emblem partial enlarged drawing after super-resolution method of the present invention is handled;
Fig. 4 (e) is the emblem partial enlarged drawing after the PR algorithm process;
Fig. 4 (f) is the emblem partial enlarged drawing after the LI method is handled.
Embodiment
The thinking of super-resolution method of the present invention is: from the hypothesis based on the border linear model, low-resolution image can be regarded as object has the combination of Polygonal Boundary, from low-resolution image, extract the boundary sections expression formula of original objects, then in high-definition picture, borderline pixel is carried out accurately painted, all the other non-boundary pixels carry out painted according to the conventional method.
Below in conjunction with accompanying drawing super-resolution method of the present invention is elaborated.
1.Exact super-resolution method
The flow process of super-resolution method Exact of the present invention as shown in Figure 1, concrete steps are as follows:
The first step to given low-resolution image, at first finds out the pixel of those existence in abutting connection with the same color neighbours, handles these pixels with the PR method;
Second step, the boundary pixel that finds all to meet one of several situations described above, and calculate the expression formula of the boundary line of passing these pixels.
The 3rd step reached formula to the boundary line segment table and carries out approximate processing, and it is continuous, smooth to make them couple together the border that forms.To all (horizontal direction, vertical direction) adjacent boundary pixels,, the mid point of two intersection points as the border intersection point of two boundary pixels on this limit, is recomputated the boundary line expression formula of this position if the boundary sections intersection point on the common edge does not overlap.
The 4th goes on foot, and utilizes the boundary line expression formula of gained, directly calculates the gray-scale value of respective pixel in the high-definition picture;
The 5th step was labeled as the gradual change pixel to all remaining pixels, handled with the LI method;
2. calculate on the border
For the expression formula of described computation bound line of second step, all remaining pixels after the first step handled are done following check, determine whether this pixel is boundary pixel, suppose the straight line boundary line be about two different color gray-scale values be respectively C lAnd C r, as shown in Figure 2.Then the gray-scale value of several pixels of running through of this boundary line (as figure) is by following formula decision.
c b = a + b 2 · c l + ( 1 - a + b 2 ) · c r .
c t = b + c 2 · c l + ( 1 - b + c 2 ) · c r .
It is known again,
b = a + c 2 ,
The substitution following formula obtains,
a = 3 c b - c t - 2 c r 2 ( c l - c r )
b = c b + c t - 2 c r 2 ( c l - c r ) .
Promptly according to given C l, C r, C bAnd C t, can calculate a, b, c.If a of gained, b, c are not less than 0, are not more than 1, think that then this pixel is boundary pixel and the formula that embodies that obtains boundary straight line.
Intersect situation for other boundary sections and pixel, for example, linear barrier's level pass pixel, the present invention can be revolved the example that provides and be turn 90 degrees.
Adopt Exact super-resolution method of the present invention that image in the real world has been done many experiments, list several results at this.
Pattern shown in Figure 3 is light and dark polygon, so all pattern boundaries all are straight lines, by different disposal routes, can find the image that high-definition picture quality that the Exact super-resolution method obtains obtains apparently higher than classic method.
The olympic logo pattern that employing distinct methods shown in Figure 4 is handled is not though the border of this pattern is a straight line.Yet after the Exact super-resolution method is handled, still can obviously be better than the visual effect of classic method.Reason is under high definition case, and the polygonal profile section that fine and closely woven short line segment is formed is the approximating curve profile very.

Claims (2)

1. the super-resolution method based on reconstruction is characterized in that it comprises the steps:
Step 1: in given low-resolution image, at first seek the pixel of those existence, duplicate the PR method with pixel then and handle these pixels, obtain the gray-scale value C of each newly-generated corresponding high-resolution pixel in abutting connection with same color;
Step 2: distinguish boundary pixel and gradual change pixel, boundary pixel is calculated the expression formula of the boundary sections of passing their pixels; The gradual change pixel is gone to step 5 to be handled;
Step 3: the boundary line segment table is reached formula carry out approximate processing, make the intersection point of adjacent boundary line segment overlap, guarantee to couple together that the border that forms is continuous, smooth; If the boundary sections intersection point on the common edge does not overlap, then with the mid point of two intersection points as the border intersection point of two boundary pixels on this limit, go to step 2, recomputate the boundary line expression formula of this position;
Step 4: utilize the boundary line expression formula of gained, and the gray-scale value of both sides, border object, directly calculate the gray-scale value of respective pixel in the high-definition picture;
Step 5: all remaining pixels are labeled as the gradual change pixel, handle with linear interpolation LI method.
2. super-resolution method as claimed in claim 1 is characterized in that: described differentiation boundary pixel of step 2 and gradual change pixel, and the method step that boundary pixel is calculated the expression formula of passing their pixel boundary line segments is as follows:
Step 21: the approximate match in the boundary line of two different pixels is become straight-line segment with the surface level oblique, and the left and right sides of described straight-line segment is the color of two different pixels, and its gray-scale value is respectively C lAnd C rFor the boundary line of level, can be clockwise or be rotated counterclockwise 90 ° with described image;
Step 22: get the unit grid square for described straight-line segment, the length of side of each grid is 0.5, makes 3 abscissa value that straight-line segment and adjacent two grids intersect be respectively a, b and c;
Step 23: set up two grey scale pixel value C that straight-line segment passes through adjacent two along slope coordinate lattice bAnd C tFormula;
Step 24: utilize the geometric relationship of a, b and c, try to achieve a, b and c value; If 0<a, b, c<1, then described two different pixels are boundary pixel, thereby obtain the formula that embodies of boundary straight line section; Otherwise described two different pixels are the gradual change pixel.
CN2009100563072A 2009-08-12 2009-08-12 Super-resolution method based on reconstruction Expired - Fee Related CN101996393B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN2009100563072A CN101996393B (en) 2009-08-12 2009-08-12 Super-resolution method based on reconstruction

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN2009100563072A CN101996393B (en) 2009-08-12 2009-08-12 Super-resolution method based on reconstruction

Publications (2)

Publication Number Publication Date
CN101996393A true CN101996393A (en) 2011-03-30
CN101996393B CN101996393B (en) 2012-08-01

Family

ID=43786516

Family Applications (1)

Application Number Title Priority Date Filing Date
CN2009100563072A Expired - Fee Related CN101996393B (en) 2009-08-12 2009-08-12 Super-resolution method based on reconstruction

Country Status (1)

Country Link
CN (1) CN101996393B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104717925A (en) * 2012-09-27 2015-06-17 富士胶片株式会社 Image processing device, method, and program
CN108305216A (en) * 2018-03-15 2018-07-20 信阳师范学院 A kind of image magnification method of bilateral four interpolation

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101226631B (en) * 2007-12-12 2010-06-09 华为技术有限公司 Super-resolution image reconstruction method and apparatus

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104717925A (en) * 2012-09-27 2015-06-17 富士胶片株式会社 Image processing device, method, and program
CN108305216A (en) * 2018-03-15 2018-07-20 信阳师范学院 A kind of image magnification method of bilateral four interpolation
CN108305216B (en) * 2018-03-15 2021-07-30 嘉兴学院 Image amplification method of bilateral quartic interpolation

Also Published As

Publication number Publication date
CN101996393B (en) 2012-08-01

Similar Documents

Publication Publication Date Title
Meng et al. High-dimensional dense residual convolutional neural network for light field reconstruction
Chen et al. Real-world single image super-resolution: A brief review
Zhao et al. Simultaneous color-depth super-resolution with conditional generative adversarial networks
Shen et al. Depth-aware image seam carving
Cheng et al. Light field super-resolution by jointly exploiting internal and external similarities
Jin et al. Occlusion-aware unsupervised learning of depth from 4-d light fields
Yin et al. Visual attention dehazing network with multi-level features refinement and fusion
KR102311796B1 (en) Method and Apparatus for Deblurring of Human Motion using Localized Body Prior
Luvizon et al. Adaptive multiplane image generation from a single internet picture
Zhu et al. Stereoscopic image super-resolution with interactive memory learning
Iwatsuki et al. Unsupervised disparity estimation from light field using plug-and-play weighted warping loss
Jin et al. Light field reconstruction via deep adaptive fusion of hybrid lenses
Lai et al. Hyperspectral Image Super Resolution With Real Unaligned RGB Guidance
CN101996393B (en) Super-resolution method based on reconstruction
Chen et al. Video super-resolution network using detail component extraction and optical flow enhancement algorithm
Fan et al. Learning Bilateral Cost Volume for Rolling Shutter Temporal Super-Resolution
Li et al. RGSR: A two-step lossy JPG image super-resolution based on noise reduction
Zhu et al. Breaking the spatio-angular trade-off for light field super-resolution via lstm modelling on epipolar plane images
Luo et al. Stereo matching and occlusion detection with integrity and illusion sensitivity
Zhang et al. Reinforcing local structure perception for monocular depth estimation
Zhuang et al. Dimensional transformation mixer for ultra-high-definition industrial camera dehazing
Yue et al. Reference guided image super-resolution via efficient dense warping and adaptive fusion
CN114240979A (en) Sub-pixel edge extraction algorithm based on deep learning for high-resolution image
Zhang et al. SivsFormer: Parallax-aware transformers for single-image-based view synthesis
Zhu et al. Accurate disparity estimation in light field using ground control points

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
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20120801

Termination date: 20150812

EXPY Termination of patent right or utility model