CN108389161A - A kind of rainy day video image separation method based on alternative sparse coding - Google Patents
A kind of rainy day video image separation method based on alternative sparse coding Download PDFInfo
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims 1
- 238000000034 method Methods 0.000 description 13
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- 238000004458 analytical method Methods 0.000 description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding
- G06T9/001—Model-based coding, e.g. wire frame
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- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
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- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30232—Surveillance
Abstract
The present invention relates to a kind of rainy day video image separation method based on alternative sparse coding, regard the separation of true picture and rainwater as Signal separator problem, the inherent attribute difference between true picture layer and rain layer is utilized in separation process, by the alternative for constraining sparse coding coefficient, a dictionary with alternative is obtained in continuous iterative learning, to which the rain layer in rainy day video image to be gradually disengaged with true picture layer in iteration.The present invention can be such that rainy day video image is more clear while reaching true picture with rain tomographic image separating effect.
Description
Technical field
The present invention relates to the technical field of Computer Vision more particularly to a kind of rain based on alternative sparse coding
Its video image separation method.
Background technology
With the propulsion of the projects such as the development of broadband network technology and safe city, Video Supervision Technique is widely applied
In various fields such as education, government, security protection, traffic, movements, and with the raising of video processing technique, Video Supervision Technique
Also gradually increase in outdoor application scenarios.Since outdoor environment is frequently necessary to face the bad weathers such as sleet, and this kind severe
Weather conditions have imponderable damage to the feature of video image, all such as the visibility of foggy weather hypograph, contrast
It can be severely impacted, and sleety weather may cause parts of images to be blocked, to influence to be further processed to image.
It is the processing to rainy day video mostly in existing data, the redundancy properties of consecutive frame contribute to the inspection of rain in video
It surveys, is also capable of providing abundant information and repairing recovery is carried out to the rainy region detected.However the redundancy of this consecutive frame is special
Property may be destroyed because of the object of video medium-high speed sports, therefore consider how from rainy day video image eliminate rain pair
The damage of characteristics of image separates rain tomographic image with important application value.
Garg and Nayar is in document [Garg, K., S.K.Nayar.Detection and removal of rain
from videos.IEEE Conference on Computer Vision&Pattern Recognition.2004.p.I-
528-I-535Vol.1.] in, analysis description has been carried out to the physical characteristic of rain.Zhang et al. [Zhang, X., H.Li, Y.Qi,
et al.Rain Removal in Video by Combining Temporal and Chromatic
Properties.IEEE International Conference on Multimedia and Expo.2006.p.461-
464.] time response and colorimetric properties that are presented in video by means of rain carries out the detection of rain.Both methods is basic
On be by the colouring information of rain come the presence of detection image moderate rain, in document [Brewer, N., N.Liu, Using the
Shape Characteristics of Rain to Identify and Remove Rain from Video.2008:
Springer Berlin Heidelberg.451-458.] in, Nathan and Liu et al. people then mainly use the shape of raindrop special
It levies to be detected to rain.Bossu et al. [Bossu, J., N.Hautiere, J.P.Tarel, Rain or Snow in the literature
Detection in Image Sequences Through Use of a Histogram of Orientation of
Streaks.International Journal of Computer Vision,2011.93(3):P.348-367.], mainly
It has inquired into how to detect and whether there is rain in video.Shaodi et al. document [You, S., R.T.Tan, R.Kawakami,
etal.Adherent Raindrop Detection and Removal in Video.IEEE Conference on
Computer Vision＆Pattern Recognition.2013.p.1035-1042.] in when considering rainy, raindrop splash
The processing of captured video on camera lens.Santhaseelan et al. [Santhaseelan, V., V.K.Asari,
Utilizing Local Phase Information to Remove Rain from Video.International
Journal of Computer Vision,2014.112(1):P.71-89.] propose using phase equalization feature come pair
Rain is detected.The detection process or repair process of the above method either video are required for adjacent frame to provide information, no
It can be suitably used for the video image of high-speed moving object.
Take the mode of dual system, some methods to use two fixations in existing document about the separation of picture signal more
System [Starck.J.L., Y.Moudden, J.Bobin, et al.Morphological component
analysis.Optics&Photonics.2005.International Society for Optics and
Photonics.p.59140Q-59140Q-15.] signal is portrayed to detach, and some methods pass through adaptive
Handwriting practicing allusion quotation come the method that carries out Signal separator, to the study of dictionary generally require a certain layer in two tomographic images similar templates or
Person according to user specifies the corresponding position of two tomographic images in the picture and is initialized to dictionary to extract regional area
[Peyré,G.,J.Fadili,J.-L.Starck,Learning the morphological diversity[J].SIAM
Journal on Imaging Sciences,2010.3(3):p.646-669.].However for rainy day video image, people
Be difficult in advance the rain figure that is detached the dictionary of rain is initialized, be also not present some area in rainy day video image
Domain is entirely rain, can extract the initialization as dictionary, therefore, it is difficult in the conventional mode to rain layer and image layer
Two different dictionaries or system of study are allowed to detach to portray two tomographic images respectively.
Invention content
It is an object of the invention to overcome the deficiencies of the prior art and provide one kind to reach true picture and rain tomographic image point
From effect, the rainy day video image separation method based on alternative sparse coding for making rainy day video image be more clear.
To achieve the above object, technical solution provided by the present invention is：
It regards the separation of true picture and rainwater as Signal separator problem, true picture layer and rain is utilized in separation process
Inherent attribute difference between layer, by constraining the alternative of sparse coding coefficient, one is obtained in continuous iterative learning has
Rain layer in rainy day video image is gradually disengaged with true picture layer in iteration by the dictionary of alternative, and it is clear to finally reach
The effect of image.
Further, separation is as follows：
S1, the picture frame in rainy day video image is extracted, the operation of S2-S6 is carried out to each frame image；
S2, initialization dictionary D1, rain block and the corresponding code coefficient of true picture blockWithThe stacking matrix of rain block
With the stacking matrix of true picture blockIndicator variable w1；(iterations l is 0 at this time, does not pass through iteration)
S3, more rain in early spring block and the code coefficient corresponding to true picture block in the way of sparse bayesian learning, obtain newest
Code coefficientWith
S4, the newest code coefficient of rain block is utilizedWith dictionary D1Rain block is reconstructed, then Hui Yu is reconstructed from rain block
Layer, obtains the brightness R of rain layerl+1；
S5, the brightness R according to rain layerl+1The brightness I of true picture layer is calculated with rain map generalization modell+1；
S6, the brightness R by rain layerl+1, true picture layer brightness Il+1, newest volume corresponding to rain block and image block
Code coefficientWithUpdate dictionary D1And update instruction variable w1；If dictionary D1Not changing or changing can ignore, then
S7 is entered step, otherwise return to step S3；
S7, true picture layer is reconfigured into back video.
Further, step S3 more rain in early spring block and coding system corresponding to true picture block in the way of sparse bayesian learning
Number, obtains newest code coefficientWithIt is as follows：
The dictionary D obtained by previous step iteration1, the stacking matrix of rain block and image blockWithIndicator variable w1, dilute
Dredge degree TI, calculate the newest code coefficient of true picture blockFormula is as follows：
Wherein, C [:, j] represent sparse coefficient jth row；
According to the newest code coefficient of true picture blockIndicator variable w1With dictionary D1, remaining rain in abstract image
Measure rl+1, formula is as follows：
And by remaining rainfall rl+1It is superimposed in last round of rain tomographic image, the newest coding of rain block is carried out using sparse bayesian learning
CoefficientCalculating, formula is as follows：
Wherein, TRFor the degree of rarefication of rain block.
Further, step S4 obtains the brightness R of rain layer and is as follows：
Utilize the newest code coefficient of rain blockWith dictionary D1Rain block is reconstructed, new rain block matrix is obtained
Calculation formula is as follows：
Rain block matrixThe corresponding brightness R of rain tomographic image is obtained after recombinationl+1。
Further, step S5 is according to the brightness R of rain layerl+1, the rain figure J and rain map generalization model of input are calculated very
The brightness I of real image layerl+1, formula is as follows：
Il+1:=(J-Rl+1)./(1-Rl+1)。
Further, step S6 updates dictionary D1It is as follows：
First by the brightness R of rain layerl+1With the brightness I of true picture layerl+1Operator P, which is stacked, by operation is converted to stacking
Matrix-block, calculation formula are as follows：
Again by solving optimal model, dictionary updating is obtained；Optimal model is as follows：
Further, update instruction variable w in the step S61, obtain newest indicator variable wl+1；Specific steps are such as
Under：
According to the newest code coefficient of rain blockNew indicator variable is generated, code coefficient is worked asThe corresponding row
On when having nonzero value, it indicates that variable is set as 1 in the point, shows that the corresponding dictionary atom of the row can be divided to rain layer, on the contrary
Indicator variable is set as 0 in the point；
Define newest indicator variable wl+1For a upper indicator variable w1Subset, i.e.,：
wl+1=wl+1.*wl。
Traditional scheme is to learn two different dictionaries or system respectively to rain layer and true picture layer to portray two layers
Image is allowed to detach.
Compared with traditional scheme, this programme principle is as follows：
It regards the separation of true picture and rainwater as Signal separator problem, true picture layer and rain is utilized in separation process
Inherent attribute difference between layer, by constraining the alternative of sparse coding coefficient, one is obtained in continuous iterative learning has
Rain layer in rainy day video image is gradually disengaged with true picture layer in iteration by the dictionary of alternative, and it is clear to finally reach
The effect of image.
Compared with traditional scheme, this programme makes rainy day video while reaching true picture and rain tomographic image separating effect
Image is more clear.
Description of the drawings
Fig. 1 is a kind of flow chart of the rainy day video image separation method based on alternative sparse coding of the present invention.
Specific implementation mode
The present invention is further explained in the light of specific embodiments：
Shown in attached drawing 1, a kind of rainy day video image separation based on alternative sparse coding described in the present embodiment
Method includes the following steps：
S1, the picture frame in rainy day video image is extracted, the operation of S2-S6 is carried out to each frame image J.
S2, initialization dictionary D1, rain block and the corresponding code coefficient of true picture blockWithThe stacking matrix of rain blockWith the stacking matrix of true picture blockIndicator variable w1。
S3, more rain in early spring block and the code coefficient corresponding to true picture block in the way of sparse bayesian learning, obtain newest
Code coefficientWith
Detailed process is：
The dictionary D obtained by previous step iteration1, the stacking matrix of rain block and image blockWithIndicator variable w1, dilute
Dredge degree TI, calculate the newest code coefficient of true picture blockFormula is as follows：
Wherein, C [:, j] represent sparse coefficient jth row；
According to the newest code coefficient of true picture blockIndicator variable w1With dictionary D1, remaining rain in abstract image
Measure rl+1, formula is as follows：
And by remaining rainfall rl+1It is superimposed in last round of rain tomographic image, the newest coding of rain block is carried out using sparse bayesian learning
CoefficientCalculating, formula is as follows：
S4, the newest code coefficient of rain block is utilizedWith dictionary D1Rain block is reconstructed, then Hui Yu is reconstructed from rain block
Layer, obtains the brightness R of rain layerl+1；
Detailed process is：
Utilize the newest code coefficient of rain blockWith dictionary D1Rain block is reconstructed, new rain block matrix is obtained
Calculation formula is as follows：
Rain block matrixThe corresponding brightness R of rain tomographic image is obtained after recombinationl+1。
S5, the brightness R according to rain layerl+1, the rain figure J and rain map generalization model of input calculate the bright of true picture layer
Spend Il+1, calculation formula is as follows：
Il+1:=(J-Rl+1)./(1-Rl+1)。
S6, the brightness R by rain layerl+1, true picture layer brightness Il+1, newest volume corresponding to rain block and image block
Code coefficientWithUpdate dictionary D1And update instruction variable w1；
Wherein, update dictionary D1Detailed process it is as follows：
By the brightness R of rain layerl+1With the brightness I of true picture layerl+1The matrix-block of stacking is converted to by operation operator P：
Again by solving optimal model, dictionary updating is obtained；Optimal model is as follows：
Update instruction variable w1, obtain newest indicator variable wl+1；It is as follows：
According to the newest code coefficient of rain blockNew indicator variable is generated, code coefficient is worked asOn the corresponding row
When having nonzero value, it indicates that variable is set as 1 in the point, shows that the corresponding dictionary atom of the row can be divided to rain layer, otherwise refers to
Show that variable is set as 0 in the point；
Define newest indicator variable wl+1For a upper indicator variable w1Subset, i.e.,：
wl+1=wl+1.*wl。
If dictionary D1Not changing or changing can ignore, then enters step S7, otherwise return to step S3.
S7, true picture layer is reconfigured into back video.
The present embodiment avoids explicitly detection and repairing, regards the separation of true picture and rainwater as Signal separator and asks
Topic, using the inherent attribute difference between true picture layer and rain layer in separation process, by constraining sparse coding coefficient
Alternative obtains a dictionary for having alternative in continuous iterative learning, and the rain layer in rainy day video image is schemed with true
As layer is gradually disengaged in iteration, the effect of clear image is finally reached.
The examples of implementation of the above are only the preferred embodiments of the invention, and the implementation model of the present invention is not limited with this
It encloses, therefore changes made by all shapes according to the present invention, principle, should all cover within the scope of the present invention.
Claims (7)
1. a kind of rainy day video image separation method based on alternative sparse coding, it is characterised in that：By true picture and rain
Signal separator problem is regarded in the separation of water as, and the inherent attribute difference between true picture layer and rain layer is utilized in separation process,
By constraining the alternative of sparse coding coefficient, a dictionary for having alternative is obtained in continuous iterative learning, will be regarded the rainy day
Rain layer in frequency image is gradually disengaged with true picture layer in iteration, finally reaches the effect of clear image.
2. a kind of rainy day video image separation method based on alternative sparse coding according to claim 1, feature
It is：Separation is as follows：
S1, the picture frame in rainy day video image is extracted, the operation of S2-S6 is carried out to each frame image J；
S2, initialization dictionary D1, rain block and the corresponding code coefficient of true picture blockWithThe stacking matrix of rain blockWith it is true
The stacking matrix of real image blockIndicator variable w1；
S3, more rain in early spring block and the code coefficient corresponding to true picture block in the way of sparse bayesian learning, obtain newest coding
CoefficientWith
S4, the newest code coefficient of rain block is utilizedWith dictionary D1Rain block is reconstructed, then rain layer is reconstructed back from rain block, is obtained
To the brightness R of rain layerl+1；
S5, the brightness R according to rain layerl+1The brightness I of true picture layer is calculated with rain map generalization modell+1；
S6, the brightness R by rain layerl+1, true picture layer brightness Il+1, newest coding system corresponding to rain block and image block
NumberWithUpdate dictionary D1And update instruction variable w1；If dictionary D1Not changing or changing can ignore, then enters
Step S7, otherwise return to step S3；
S7, true picture layer is reconfigured into back video.
3. a kind of rainy day video image separation method based on alternative sparse coding according to claim 2, feature
It is：The step S3 more rain in early spring block and code coefficient corresponding to true picture block in the way of sparse bayesian learning, obtain most
New code coefficientWithIt is as follows：
Pass through dictionary D1, the stacking matrix of rain block and image blockWithIndicator variable w1, degree of rarefication TICalculate true picture block
Newest code coefficientFormula is as follows：
Wherein, C [:, j] represent sparse coefficient jth row；
According to the newest code coefficient of true picture blockIndicator variable w1With dictionary D1, remaining rainfall r in abstract imagel +1, formula is as follows：
And by remaining rainfall rl+1It is superimposed in last round of rain tomographic image, the newest code coefficient of rain block is carried out using sparse bayesian learningCalculating, formula is as follows：
Wherein, TRFor the degree of rarefication of rain block.
4. a kind of rainy day video image separation method based on alternative sparse coding according to claim 2, feature
It is：The brightness R that the step S4 obtains rain layer is as follows：
Utilize the newest code coefficient of rain blockWith dictionary D1Rain block is reconstructed, new rain block matrix is obtainedIt calculates
Formula is as follows：
Rain block matrixThe corresponding brightness R of rain tomographic image is obtained after recombinationl+1。
5. a kind of rainy day video image separation method based on alternative sparse coding according to claim 2, feature
It is：The step S5 is according to the brightness R of rain layerl+1, the rain figure J and rain map generalization model that are originally inputted calculate true figure
As the brightness I of layerl+1, formula is as follows：
Il+1:=(J-Rl+1)./(1-Rl+1)。
6. a kind of rainy day video image separation method based on alternative sparse coding according to claim 2, feature
It is：The step S6 update dictionaries D1It is as follows：
First by the brightness R of rain layerl+1With the brightness I of true picture layerl+1The matrix that operator P is converted to stacking is stacked by operation
Block, formula are as follows：
Again by solving optimal model, dictionary updating is obtained；Optimal model is as follows：
7. a kind of rainy day video image separation method based on alternative sparse coding according to claim 2, feature
It is：Update instruction variable w in the step S61, obtain newest indicator variable wl+1；It is as follows：
According to the newest code coefficient of rain blockNew indicator variable is generated, code coefficient is worked asHave on the corresponding row non-
When zero, it indicates that variable is set as 1 in the point, shows that the corresponding dictionary atom of the row can be divided to rain layer, otherwise instruction becomes
Amount is set as 0 in the point；
By newest indicator variable wl+1It is defined as an indicator variable w1Subset, i.e.,：
wl+1=wl+1.*wl。
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|---|---|---|---|---|
| CN103489203A (en) * | 2013-01-31 | 2014-01-01 | 清华大学 | Image coding method and system based on dictionary learning |
| CN103700070A (en) * | 2013-12-12 | 2014-04-02 | 中国科学院深圳先进技术研究院 | Video raindrop-removing algorithm based on rain-tendency scale |
| CN104574456A (en) * | 2014-12-01 | 2015-04-29 | 南昌大学 | Graph regularization sparse coding-based magnetic resonance super-undersampled K data imaging method |
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2018
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103489203A (en) * | 2013-01-31 | 2014-01-01 | 清华大学 | Image coding method and system based on dictionary learning |
| CN103700070A (en) * | 2013-12-12 | 2014-04-02 | 中国科学院深圳先进技术研究院 | Video raindrop-removing algorithm based on rain-tendency scale |
| CN104574456A (en) * | 2014-12-01 | 2015-04-29 | 南昌大学 | Graph regularization sparse coding-based magnetic resonance super-undersampled K data imaging method |
Non-Patent Citations (1)
| Title |
|---|
| YU LUO ET AL: ""Removing rain from a single image via discriminative sparse coding"", 《IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)》 * |
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Application publication date: 20180810 |