CN104021533B - A kind of real time imaging noise-reduction method and device - Google Patents
A kind of real time imaging noise-reduction method and device Download PDFInfo
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Abstract
The invention discloses a kind of real time imaging noise-reduction method and device, image for gathering to image capture device carries out noise reduction, the method, for each pixel of input picture, finds corresponding Noise gate threshold value in image capture device current gain value corresponding picture noise envelope curve；Calculate the difference of neighbor pixel and this pixel in each neighborhood of pixel points, the absolute value of difference is less than the neighbor pixel participation noise reduction of Noise gate threshold value, is otherwise not involved in noise reduction；Using the neighbor pixel participating in noise reduction in its neighborhood, noise reduction process is carried out to each pixel.The present invention further simultaneously discloses the device realizing said method, and this device includes table look-up module, detection module and noise reduction module, still further comprises picture noise envelope curve demarcating module.The method of the present invention and device, processing speed is fast, and hardware realizes that resource overhead is little, and it is convenient to realize, and noise reduction is with strong points, and effect is good.
Description
Technical field
The invention belongs to technical field of image processing, especially design a kind of method in image acquisition end real time imaging noise reduction
And device.
Background technology
Image is during collection, transmission, storage etc. usually because being made picture quality have by the interference of each noise like
Declined, thus having a negative impact to follow-up image procossing, therefore in image processing field image noise reduction be one non-
Often important research topic.Image noise reduction is removal noise contribution from existing noisy image, improves image image quality.Figure
As collecting device such as web camera (IPC), digital camera etc., after target object is shot, through colour filter battle array
The RAW data that row (CFA) are captured by, needs to carry out image noise reduction.
General pattern noise reduction can be divided into 2D noise reduction and 3D noise reduction, and 2D noise reduction is only carried out at noise reduction on two-dimensional space domain
Reason；And 3D noise reduction, add Time Domain Processing, be therefore changed into three-dimensional.The basic skills of 2D noise reduction be to a pixel by its with week
Enclose pixel average, averagely rear noise reduces, and its shortcoming is to cause fuzzy pictures, particularly object edge part.Therefore to this
The improvement planting noise-reduction method is mainly by rim detection, and the pixel of marginal portion is not used to carry out noise reduction.3D noise reduction and 2D fall
The difference made an uproar is, 2D noise reduction only considers a two field picture, and 3D noise reduction considers the temporal relationship between frame and frame further, to every
Individual pixel carries out average in time domain, by reducing the change reduction noise in time domain.Compare 2D noise reduction, 3D noise reduction is more
Good, and do not result in the fuzzy of edge, but the subject matter existing is：Picture will not be totally stationary, if to being not belonging to
Two points of same object carry out noise reduction process and can cause mistake.Therefore the method needs estimation, its effect quality also with
Estimation is related.And estimation is computationally intensive, time-consuming, is the Main Bottleneck of restriction 3D noise reduction.
The method of 2D noise reduction is a lot, and most basic method is medium filtering based on rim detection, mean filter, this kind of side
Although method realizes simple, very difficult balance " reservation of marginal information " and " removal of noise ".Relatively customary way also has
Gaussian filtering, bilateral filtering, but this kind of method is typically implemented more complicated, and it is big that resource overhead realized by hardware.
Because 2D noise reduction implements relatively easily, therefore how 2D noise reduction is improved, remain current research
One direction.
Content of the invention
It is an object of the invention to provide a kind of real time imaging noise-reduction method and device, for gather to image capture device
Image carries out noise reduction, noise-reduction method of the prior art can be avoided to realize more complicated, and big the asking of resource overhead realized by hardware
Topic, and noise reduction is with strong points, realizes real-time noise-reducing.
To achieve these goals, technical solution of the present invention is as follows：
A kind of real time imaging noise-reduction method, the image for gathering to image capture device carries out noise reduction, and the method includes
Following steps：
For each pixel of input picture, in image capture device current gain value corresponding picture noise envelope
Corresponding Noise gate threshold value is found in curve；
Calculate the difference of neighbor pixel and this pixel in each neighborhood of pixel points, the absolute value of difference is less than Noise gate
The neighbor pixel of threshold value participates in noise reduction, is otherwise not involved in noise reduction；
Using the neighbor pixel participating in noise reduction in its neighborhood, noise reduction process is carried out to each pixel.
Further, the preparation method of described image noise envelope curve includes step：
Uncalibrated image collecting device different gains corresponding noise criteria difference δ；
According to the noise reduction intensity setting, set up the picture noise envelope curve corresponding to input picture according to equation below：
NoiseThr (y, X)=max ((f-1(f(y)+X)-y),(y-f-1(f(y)-X)))；
Wherein y is the pixel value of pixel in input picture, and X is the noise intensity of pixel, and X=K* δ, K are that noise reduction is strong
Degree, NoiseThr (y, X) is the corresponding maximum noise of pixel, and f (y) is mapped to, for input picture, the gamma that display end shows and reflects
Penetrate function, f (y)=y^ (1/r), r are gamma coefficient.
Further, for each pixel of input picture, its corresponding Noise gate threshold value is exactly that described image is made an uproar
On sound envelope curve, to should pixel pixel value maximum noise NoiseThr (y, X), y is the pixel value of this pixel.
A kind of implementation of the present invention, the described neighbor that each pixel is utilized with participation noise reduction in its neighborhood
Point carries out noise reduction process, including step：
For each pixel, calculate the meansigma methodss of the neighbor pixel participating in noise reduction and its difference；
This meansigma methods is added on this pixel, as the pixel value after this pixel noise reduction.
Another kind of implementation of the present invention, the described adjacent picture that each pixel is utilized with participation noise reduction in its neighborhood
Vegetarian refreshments carries out noise reduction process, including step：
For each pixel, for participating in the neighbor pixel distribution noise reduction weight of noise reduction, and calculate this noise reduction weight with
Described difference long-pending；
Calculate long-pending meansigma methodss, this meansigma methods is added on this pixel, as the pixel value after this pixel noise reduction.
The invention allows for a kind of real time imaging denoising device, for dropping to the image that image capture device gathers
Make an uproar, this device includes：
Table look-up module, for each pixel for input picture, corresponds in image capture device current gain value
Picture noise envelope curve in find corresponding Noise gate threshold value；
Detection module, for calculating the difference of neighbor pixel and this pixel in each neighborhood of pixel points, difference exhausted
Less than the neighbor pixel of Noise gate threshold value, noise reduction is participated in value, is otherwise not involved in noise reduction；
Noise reduction module, for being carried out at noise reduction using the neighbor pixel participating in noise reduction in its neighborhood to each pixel
Reason.
Further, described device also includes picture noise envelope curve demarcating module, described image noise envelope curve
Demarcating module is used for the picture noise envelope curve of uncalibrated image collecting device, and the picture noise envelope curve demarcated is inputted
To described searching modul, described image noise envelope calibration curve module is in the picture noise envelope song of uncalibrated image collecting device
Following steps are executed during line：
Uncalibrated image collecting device different gains corresponding noise criteria difference δ；
According to the noise reduction intensity setting, set up the picture noise envelope curve corresponding to input picture according to equation below：
NoiseThr (y, X)=max ((f-1(f(y)+X)-y),(y-f-1(f(y)-X)))；
Wherein y is the pixel value of pixel in input picture, and X is the noise intensity of pixel, and X=K* δ, K are that noise reduction is strong
Degree, NoiseThr (y, X) is the corresponding maximum noise of pixel, and f (y) is mapped to, for input picture, the gamma that display end shows and reflects
Penetrate function, f (y)=y^ (1/r), r are gamma coefficient.
Further, for each pixel of input picture, its corresponding Noise gate threshold value is exactly that described image is made an uproar
On sound envelope curve, to should pixel pixel value maximum noise NoiseThr (y, X), y is the pixel value of this pixel.
A kind of implementation of the present invention, described noise reduction module, when carrying out image noise reduction process, executes following steps：
For each pixel, calculate the meansigma methodss of the neighbor pixel participating in noise reduction and its difference；
This meansigma methods is added on this pixel, as the pixel value after this pixel noise reduction.
Another kind of implementation of the present invention, described noise reduction module, when carrying out image noise reduction process, executes following steps：
For each pixel, for participating in the neighbor pixel distribution noise reduction weight of noise reduction, and calculate this noise reduction weight with
Described difference long-pending；
Calculate long-pending meansigma methodss, this meansigma methods is added on this pixel, as the pixel value after this pixel noise reduction.
The present invention proposes a kind of real time imaging noise-reduction method and device, by demarcating the picture noise envelope song of collection terminal
Line, and the maximum noise of each pixel is corresponded to using on this curve as noise reduction threshold value.Noise reduction process directly passes through
The method tabled look-up obtains this noise reduction threshold value to carry out image noise reduction it is not necessary to go to judge flat region and edge using Noise Estimation
Area, processing speed is fast, and hardware realizes that resource overhead is little, and it is convenient to realize, and the method excellent noise reduction effect.
Brief description
Fig. 1 is the mapping relations figure at image acquisition end to display end；
Fig. 2 is collection terminal picture noise envelope curve schematic diagram；
Fig. 3 is a kind of present invention real time imaging noise-reduction method flow chart；
Fig. 4 is present invention pixel vertex neighborhood schematic diagram；
Fig. 5 is a kind of present invention real time imaging denoising device structural representation.
Specific embodiment
With reference to the accompanying drawings and examples technical solution of the present invention is described in further details, following examples are not constituted
Limitation of the invention.
From collection terminal to the mapping relations of display end general satisfaction Fig. 1, in Fig. 1, y is pixel in collection terminal image to image
Pixel value, f (y) be display end image, have passed through gamma mapping curve f (y)=y^ (1/r) from collection terminal to display end, its
Middle r is gamma coefficient.
For the power of picture noise, the standard deviation of noise can be obtained by various noise estimation methods, and use standard
Differ from and to represent the power of picture noise.The theoretical basiss of do so are that the variance of image flat site should be minimum, and its side
Difference should be mainly noise variance decision, noise variance evolution is just obtained the standard deviation of noise.Traditional noise-reduction method is direct
Judge that threshold value is made comparisons by standard deviation and noise, if standard deviation is less than threshold value, explanation is flat region, can increase noise reduction
Weight, if standard deviation be more than threshold value, explanation is marginal zone, should suitably weaken noise reduction weight.
If the standard deviation of noise is represented with δ, δ is multiplied by after a COEFFICIENT K the present embodiment, obtains X=K* δ, is represented with X
The noise size of image.Ideally, image show value is f (y), and f (y) represents the image of not Noise, on this basis
After introducing noise " ± X ", display end image fluctuates between f (y)-X and f (y)+X.Display end noisy image is in f (y)-X and f
Fluctuate between (y)+X, if the anti-mapping function of gamma is f-1Y (), display image reflection is mapped to behind image acquisition end, collection terminal
Noisy image is in f-1(f (y)-X) and f-1Fluctuate between (f (y)+X).
The noise of collection terminal image can be derived further in (f-1(f (y)+X)-y) and (y-f-1(f (y)-X)) between ripple
Dynamic, defining collection terminal picture noise envelope curve is：
NoiseThr (y, X)=max ((f-1(f(y)+X)-y),(y-f-1(f(y)-X))).
As shown in Fig. 2 NoiseThr (y, X) is actually corresponding to pixel in input picture (pixel value is y)
Big noise, if with NoiseThr (y, X) for noise reduction threshold value, can effective detection noise.
The present embodiment just with NoiseThr (y, X) be noise reduction threshold value, to realize image noise reduction process, as shown in figure 3, this
A kind of embodiment real time imaging noise-reduction method comprises the steps：
Step 301, each pixel for input picture, in the corresponding image of image capture device current gain value
Corresponding Noise gate threshold value is found in noise envelope curve.
For image capture device current gain value corresponding picture noise envelope curve, can complete to mark in laboratory
Fixed, specifically include step：
1), the corresponding noise criteria of uncalibrated image collecting device different gains is poor.
For the image capture device of collection terminal, such as video camera etc., the RAW image data of its imageing sensor capture can not
The presence noise avoiding.For image capture device, its corresponding noise criteria difference δ is with the close phase of yield value of camera setting
Close, the demarcation of δ can be completed in laboratory.
For example in the gain interval of image capture device work, extract typical n yield value, calibrate each yield value
Corresponding noise criteria difference δ.Wherein for any one yield value, change illumination (corresponding to the pixel value of pixel) y obtains
Image, then obtains, by Noise Estimation, the variation relation that corresponding δ calibrates δ and illumination y.
2), according to the noise reduction intensity setting, set up the picture noise envelope curve corresponding to input picture.
According to noise reduction intensity K of user setup, obtain picture noise X=K* δ, set up input picture corresponding collection terminal figure
As noise envelope curve NoiseThr (y, X).Under the premise of certain noise intensity estimated value X, when collection terminal image pixel value is y
When, the maximum noise of image is NoiseThr (y, X), and this value can be used as noise reduction threshold value.
Hence for the image of input, corresponding picture noise bag can be found according to image capture device current gain value
Network curve, then finds corresponding Noise gate threshold value according to the pixel value y of each pixel.
The difference of neighbor pixel and this pixel in step 302, each neighborhood of pixel points of calculating, the absolute value of difference is little
Neighbor pixel in Noise gate threshold value participates in noise reduction, is otherwise not involved in noise reduction.
As shown in figure 4, for any pixel P (i, j), (i, j) is coordinate, centered on pixel P (i, j), definition
The neighborhood of pixel P (i, j) is as noise reduction template, usually 3 × 3 or 5 × 5 or 7 × 7 square.Noise reduction template is with pixel P
Pixel centered on (i, j), other pixels are referred to as its neighbor pixel.Calculate the neighbor pixel in this neighborhood and middle imago
The difference of vegetarian refreshments P (i, j), if the difference obtaining is less than Noise gate threshold value, corresponding neighbor pixel participates in central pixel point P
The noise reduction of (i, j), is otherwise not involved in noise reduction.
Here difference is the difference of pixel pixel value, and this pixel value is brightness value, i.e. its illumination y.
Step 303, to each pixel using in its neighborhood participate in noise reduction neighbor pixel carry out noise reduction process.
Specifically, the meansigma methodss of the pixel participating in noise reduction and the difference of central pixel point P (i, j) are calculated, this is average
Value is added in central pixel point P (i, j), reaches the purpose of noise reduction.
It should be noted that specific noise reduction process method does not limit and the meansigma methodss of difference is added on pixel, also
There are other noise reduction process methods, the pixel distribution noise reduction weight such as participating in noise reduction for each, then try to achieve difference again and drop with it
Long-pending, the long-pending meansigma methodss of calculating of weight of making an uproar, the meansigma methodss of calculating are added on pixel P (i, j), reach the purpose of noise reduction.
In sum, due to having demarcated the noise criteria difference δ of image capture device, and generate corresponding picture noise bag
Network curve, then can find out Noise gate threshold value with image input value, and carry out noise reduction process accordingly.It should be noted that it is right
In the different yield value of image capture device, there are different corresponding noise criteria difference δ, and its corresponding picture noise envelope
Curve, after input picture, can find corresponding picture noise envelope according to the current yield value of image capture device bent
Line, and obtain noise reduction threshold value further, by the way of tabling look-up, decrease the amount of calculation in noise reduction process, and hardware is realized
Simply.
Fig. 5 shows a kind of embodiment of the present invention real time imaging denoising device structural representation, including：
Table look-up module, for each pixel for input picture, corresponds in image capture device current gain value
Picture noise envelope curve in find corresponding Noise gate threshold value；
Detection module, for calculating the difference of neighbor pixel and this pixel in each neighborhood of pixel points, difference exhausted
Less than the neighbor pixel of Noise gate threshold value, noise reduction is participated in value, is otherwise not involved in noise reduction；
Noise reduction module, for being carried out at noise reduction using the neighbor pixel participating in noise reduction in its neighborhood to each pixel
Reason.
Further, this device also includes picture noise envelope curve demarcating module, picture noise envelope curve calibration mold
Block is used for the picture noise envelope curve of uncalibrated image collecting device, and the picture noise envelope curve of demarcation is input to described
Searching modul, described image noise envelope calibration curve module is held in the picture noise envelope curve of uncalibrated image collecting device
Row following steps：
Uncalibrated image collecting device different gains corresponding noise criteria difference δ；
According to the noise reduction intensity setting, set up the picture noise envelope curve corresponding to input picture according to equation below：
NoiseThr (y, X)=max ((f-1(f(y)+X)-y),(y-f-1(f(y)-X)))；
Wherein y is the pixel value of pixel in input picture, and X is the noise intensity of pixel, and X=K* δ, K are that noise reduction is strong
Degree, NoiseThr (y, X) is the corresponding maximum noise of pixel, and f (y) is mapped to, for input picture, the gamma that display end shows and reflects
Penetrate function, f (y)=y^ (1/r), r are gamma coefficient.
For each pixel of input picture, its corresponding Noise gate threshold value is exactly described image noise envelope curve
On, to should pixel pixel value maximum noise NoiseThr (y, X), y is the pixel value of this pixel.
In the present embodiment, noise reduction module, when carrying out image noise reduction process, executes following steps：
For each pixel, calculate the meansigma methodss of the neighbor pixel participating in noise reduction and its difference；
This meansigma methods is added on this pixel, as the pixel value after this pixel noise reduction.
Another kind of implementation when carrying out image noise reduction process for the noise reduction module includes step：
For each pixel, for participating in the neighbor pixel distribution noise reduction weight of noise reduction, and calculate this noise reduction weight with
Described difference long-pending；
Calculate long-pending meansigma methodss, this meansigma methods is added on this pixel, as the pixel value after this pixel noise reduction.
It should be noted that this device is FPGA, picture noise envelope curve demarcating module is demarcating picture noise bag
After network curve, it is issued in the searching modul of FPGA in the way of list item.
Above example only in order to technical scheme to be described rather than be limited, without departing substantially from the present invention essence
In the case of god and its essence, those of ordinary skill in the art work as and can make various corresponding changes and change according to the present invention
Shape, but these corresponding changes and deformation all should belong to the protection domain of appended claims of the invention.
Claims (8)
1. a kind of real time imaging noise-reduction method, the image for gathering to image capture device carries out noise reduction it is characterised in that being somebody's turn to do
Method comprises the steps：
For each pixel of input picture, in image capture device current gain value corresponding picture noise envelope curve
In find corresponding Noise gate threshold value；
Calculate the difference of neighbor pixel and this pixel in each neighborhood of pixel points, the absolute value of difference is less than Noise gate threshold value
Neighbor pixel participate in noise reduction, be otherwise not involved in noise reduction；
Using the neighbor pixel participating in noise reduction in its neighborhood, noise reduction process is carried out to each pixel；
Wherein, the preparation method of described image noise envelope curve includes step：
Uncalibrated image collecting device different gains corresponding noise criteria difference δ；
According to the noise reduction intensity setting, set up the picture noise envelope curve corresponding to input picture according to equation below：
NoiseThr (y, X)=max ((f-1(f(y)+X)-y),(y-f-1(f(y)-X)))；
Wherein y is the pixel value of pixel in input picture, and X is the noise intensity of pixel, and X=K* δ, K are noise reduction intensity,
NoiseThr (y, X) is the corresponding maximum noise of pixel, and f (y) is mapped to, for input picture, the gamma mapping that display end shows
Function, f (y)=y^(1/r), r is gamma coefficient.
2. real time imaging noise-reduction method according to claim 1 is it is characterised in that each pixel for input picture
Point, its corresponding Noise gate threshold value is exactly on described image noise envelope curve, to should pixel pixel value maximum noise
NoiseThr (y, X), y are the pixel value of this pixel.
3. real time imaging noise-reduction method according to claim 1 is it is characterised in that described utilize it to each pixel
The neighbor pixel participating in noise reduction in neighborhood carries out noise reduction process, including step：
For each pixel, calculate the meansigma methodss of the neighbor pixel participating in noise reduction and its difference；
This meansigma methods is added on this pixel, as the pixel value after this pixel noise reduction.
4. real time imaging noise-reduction method according to claim 1 is it is characterised in that described utilize it to each pixel
The neighbor pixel participating in noise reduction in neighborhood carries out noise reduction process, including step：
For each pixel, for participating in the neighbor pixel distribution noise reduction weight of noise reduction, and calculate this noise reduction weight with described
Difference long-pending；
Calculate long-pending meansigma methodss, this meansigma methods is added on this pixel, as the pixel value after this pixel noise reduction.
5. a kind of real time imaging denoising device, the image for gathering to image capture device carries out noise reduction it is characterised in that being somebody's turn to do
Device includes：
Searching modul, for each pixel for input picture, in the corresponding figure of image capture device current gain value
As finding corresponding Noise gate threshold value in noise envelope curve；
Detection module, for calculating the difference of neighbor pixel and this pixel in each neighborhood of pixel points, the absolute value of difference
Participate in noise reduction less than the neighbor pixel of Noise gate threshold value, be otherwise not involved in noise reduction；
Noise reduction module, for carrying out noise reduction process to each pixel using the neighbor pixel participating in noise reduction in its neighborhood；
Wherein, described device also includes picture noise envelope curve demarcating module, described image noise envelope calibration curve module
For the picture noise envelope curve of uncalibrated image collecting device, and by the picture noise envelope curve of demarcation be input to described in look into
Look for module, described image noise envelope calibration curve module executes in the picture noise envelope curve of uncalibrated image collecting device
Following steps：
Uncalibrated image collecting device different gains corresponding noise criteria difference δ；
According to the noise reduction intensity setting, set up the picture noise envelope curve corresponding to input picture according to equation below：
NoiseThr (y, X)=max ((f-1(f(y)+X)-y),(y-f-1(f(y)-X)))；
Wherein y is the pixel value of pixel in input picture, and X is the noise intensity of pixel, and X=K* δ, K are noise reduction intensity,
NoiseThr (y, X) is the corresponding maximum noise of pixel, and f (y) is mapped to, for input picture, the gamma mapping that display end shows
Function, f (y)=y^(1/r), r is gamma coefficient.
6. real time imaging denoising device according to claim 5 is it is characterised in that each pixel for input picture
Point, its corresponding Noise gate threshold value is exactly on described image noise envelope curve, to should pixel pixel value maximum noise
NoiseThr (y, X), y are the pixel value of this pixel.
7. real time imaging denoising device according to claim 5 is it is characterised in that described noise reduction module is carrying out image fall
Make an uproar process when, execute following steps：
For each pixel, calculate the meansigma methodss of the neighbor pixel participating in noise reduction and its difference；
This meansigma methods is added on this pixel, as the pixel value after this pixel noise reduction.
8. real time imaging denoising device according to claim 5 is it is characterised in that described noise reduction module is carrying out image fall
Make an uproar process when, execute following steps：
For each pixel, for participating in the neighbor pixel distribution noise reduction weight of noise reduction, and calculate this noise reduction weight with described
Difference long-pending；
Calculate long-pending meansigma methodss, this meansigma methods is added on this pixel, as the pixel value after this pixel noise reduction.
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