CN104767913B - A kind of adaptive video denoising system of contrast - Google Patents
A kind of adaptive video denoising system of contrast Download PDFInfo
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Abstract
The invention discloses a kind of adaptive video denoising system of contrast, deposited including a frame for caching filter result, one frame difference feature calculation module is used to calculating video present incoming frame and frame deposit in previous filtering frame frame difference feature, one contrast computing module is used for the local contrast for calculating present incoming frame, then low contrast regions detection module is inputted, the low contrast regions confidence level calculated and the common input motion detection module of frame difference feature, calculate the probability of motion of each pixel.Motion Adaptive time-domain filtering module deposited using video present incoming frame and frame in the input of previous filtering frame, and the probability of motion of each pixel carries out Motion Adaptive time-domain filtering, finally exports current filter frame deposit frame and deposits.Conventional video denoising system can be solved in processing low contrast the sport video motion smear and fuzzy problem that can produce using the system.
Description
Technical field
The present invention relates to technical field of video processing, more particularly to the technical field of time domain noise reduction is carried out to video.
Background technology
Since picture pick-up device (CMOS, ccd sensor) suffers from gatherer process the influence of noise, cause video often
There is random noise, and the phenomenon of noise is even more serious especially under low-light (level), so needing to utilize video denoising technology
Noise is removed.In addition as mobile interchange and video source are more and more multi-sourcing, needed in the display terminals such as TV
Play and show various video sources, the internet video shot using handheld device.The camera of handheld mobile device
Since its sensor area is limited, for the large area sensor of professional camera equipment, its image quality is poor, and noise is more
To be serious, so video denoising technology becomes to be even more important.
Vedio noise reduction technology includes spatial domain noise reduction and time domain noise reduction technology.Wherein, spatial domain noise reduction technology has simple spatial domain
Filtering such as mean filter, medium filtering, often bring the fuzzy of details.Time domain noise reduction technology due to its protection to details more
It is good, and more used by industrial quarters.Traditional time domain noise reduction method is as shown in Figure 1, utilize present incoming frame and previous filtering
Frame calculates frame difference, and then with frame difference and threshold value comparison, to carry out motion detection, i.e. frame difference is more than threshold value
Pixel is movement pixel, and the pixel that frame difference is less than threshold value is static pixel, is then instructed using the result of motion detection
To the time-domain filtering of present incoming frame and previous filtering frame, if stagnant zone, the time-domain filtering of multiframe weighting is carried out, is reached
The effect of denoising, if moving region, then without time-domain filtering.
Motion detection can generally produce Type Ⅰ Ⅱ error, and Error type I is missing inspection, that is, move pixel and be judged as still image
Element, it is this detection mistake can to moving region also using multiframe weighting time-domain filtering so that cause moving target hangover or
The distortion phenomenon that interframe obscures.Error type II is false-alarm, and static pixel is divided into movement pixel, this kind of wrong meeting of detection by mistake
Make stagnant zone without time-domain filtering, so that noise existing for stagnant zone can not be removed.If the threshold value of motion detection is high,
The mistake of missing inspection then easily occurs.If the threshold value of motion detection is low, the mistake of false-alarm easily occurs.
Traditional method for testing motion, such as patent US7903179B2 and US6061100, and patent US 2006/
The method proposed in 0139494A1, often using adaptive of overall importance of the ful-scale threshold value specified in advance or noise level
Threshold value carries out motion detection, such as patent US6061100, using threshold value of 2 times of noise levels as motion detection, if interframe
Difference is less than 2 times of noise levels, then is static pixel, is then otherwise movement pixel.This kind of method for testing motion often only considers
The characteristic statistics distribution of static pixel, when noise meets the distribution of Gauss white noise, can ensure more than 95% static pixel not by
Movement pixel is detected as, i.e., the error generation rate of Error type I, that is, false-alarm is less than 5%, but is unable to control error type II and leakage
The error rate of inspection.For low contrast sport video (the less video of luminance difference i.e. between moving target and background), this
The method that kind threshold value is chosen can cause substantial amounts of undetected error, i.e. moving region is not detected among out, thus in time-domain filtering
When can produce moving target hangover and interframe blooming, this does not have the removal problem more serious on subjective picture quality than noise.
How the undetected error of low contrast regions is controlled at the same time so that contrasted zones will not produce moving target hangover
It is problem to be solved with interframe fuzzy problem.
The content of the invention
In order to solve the undetected error of low contrast regions so that contrasted zones will not produce moving target hangover and frame
Between fuzzy problem, the present invention propose a kind of adaptive video denoising system of contrast, preferably go to make effect so as to reach
Fruit, ensure that the clarity of video.
To reach above-mentioned purpose, a kind of adaptive video denoising system of contrast proposed by the present invention, including frame is deposited, frame
Between difference characteristic computing module, motion detection block, Motion Adaptive time-domain filtering module, further include contrast computing module,
Low contrast regions detection module；The part that contrast computing module calculates output present incoming frame I according to present incoming frame meter I is right
Than degree C；Low contrast regions detection module calculates output low contrast regions according to the local contrast C of present incoming frame I and puts
Reliability R_LC；Motion detection block is according to low contrast regions confidence level R_LC and the frame of frame difference feature calculation module output
Between difference calculate output pixel probability of motion R_Motion.
The contrast computing module includes horizontal gradient computing unit, Grads threshold computing unit, intermediate zone detection
Unit, left average calculation unit, right average calculation unit, absolute difference computing unit；Horizontal gradient computing unit is used for ought
Preceding input frame is transformed to horizontal gradient image G；Grads threshold computing unit calculates output Grads threshold according to horizontal gradient image G
Gt；Intermediate zone detection unit calculates the intermediate zone mark for exporting measuring point pixel to be checked according to horizontal gradient image G, Grads threshold Gt
α, and local window around measuring point pixel to be checked is divided into left window and right window；Left average calculation unit is according to current input
Frame and non-intermediate zone mark α calculate the gray average left_mean of the output non-intermediate zone pixel of left window；Right average calculation unit
The gray average right_mean of the output non-intermediate zone pixel of right window is calculated according to present incoming frame and intermediate zone mark α；Absolutely
The gray average left_mean and the non-intermediate zone of right window of output left window non-intermediate zone pixel are calculated value difference computing unit
Local contrast C of the absolute value of the difference of the gray average right_mean of pixel as present incoming frame I.
The present invention, can be according to contrast by the adaptive movement detection systems of offer calculating local contrast and contrast
Degree adaptively determines the parameter of motion detection, so as to reach following beneficial effect：
(1) for the low contrast moving object region in low contrast sport video or video, can effectively control Lou
The generation of error detection by mistake, so as to avoid the generation of moving object hangover phenomenon under low contrast.
(2) for the high-contrast area in high contrast sport video or video, it can effectively control control false-alarm wrong
Generation, so as to ensure that such video or region have good denoising effect by mistake.
Brief description of the drawings
Video time domain noise reduction system schematic diagram traditional Fig. 1；
Moving object schematic diagram in the movement pixel statistical analysis of Fig. 2A the present embodiment；
Image schematic diagram of the moving object in t moment in the movement pixel statistical analysis of Fig. 2 B the present embodiment；
The MAE feature distributions curve one of the static pixels of Fig. 3 A and movement pixel；
The MAE feature distributions curve two of the static pixels of Fig. 3 B and movement pixel；
The adaptive video denoising system schematic of Fig. 4 the present embodiment contrasts；
Fig. 5 A the present embodiment contrast computing module schematic diagrames；
Fig. 5 B are through its row coordinate j of pixel and pixel grey scale on a horizontal line of moving object and background intersection
It is worth corresponding diagram；
Fig. 5 C the present embodiment contrast computational methods schematic diagrames；
The MAE feature distributions curve one of the static pixels of Fig. 6 A and movement pixel；
The MAE feature distributions curve two of the static pixels of Fig. 6 B and movement pixel；
The relation curve schematic diagram one of Fig. 7 A R_LC and X；
Fig. 7 B：The relation curve schematic diagram two of R_LC and X；
Fig. 7 C：The relation curve schematic diagram three of R_LC and X；
Fig. 8 soft-threshold motion detection curve synoptic diagrams.
Embodiment
For the object, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with specific embodiment, and reference
Attached drawing, the present invention is described in more detail.
1, move the statistical distribution analysis of pixel and static pixel
In conventional art such as patent US 2006/0139494A1, US6061100, the method for testing motion taken is only
The statistical distribution of static pixel is considered, so as to be unable to control the generation of undetected error.In order to control undetected error, the present invention is right
The statistical distribution of movement pixel has done following analysis, can quantitatively find out that the luminance difference of moving object and background (contrasts
Degree) influence to motion detection.
As shown in Fig. 2A, Fig. 2 B, moving object is the circle in Fig. 2A.Assuming that the gray value of background is B, moving object and
Gray scale difference, that is, contrast of background is C, noise n, then in the case where there is noise situations, its gray value of moving object is C+B+n, week
The region for enclosing white is background, and gray value B+n, noise n obey zero-mean gaussian, and noise variance isI.e.Fig. 2A is image of the video at the t-1 moment, and image 2B is image of the video in t moment.Moving object is sent out
Movement is given birth to.
The image intensity value for being located at t moment is gt, the image intensity value at t-1 moment is gt-1
The distribution of the inter-pixel difference d in the A3 regions in moving region, i.e. Fig. 2 B is as follows：
gt-1=C+B+n (1)
gt=B+n (2)
D=gt-gt-1 (3)
What then d was obeyed is distributed as：
Make y=| d |, then what the absolute difference y of inter-pixel was obeyed is distributed as
The average of y is：
The variance of y is：
When carrying out motion detection, the local mean values (Mean of the absolute difference y of generally use inter-pixel
Absolute Error, referred to as MAE) it is used as the feature of motion detection, shown in the calculating such as formula (8) of the MAE features,
Y1 ..., yk are respectively the absolute difference y of the inter-pixel of local k adjacent pixel, and the distribution that m is obeyed, its is equal
Value is identical with y, and variance is the 1/k of y variances, i.e.,
The pixel (the A2 regions of the image in t moment of such as Fig. 2 B) of stagnant zone, the distribution that its MAE feature is obeyed, is
The special circumstances of formula (9) and (10) in C=0
The MAE feature distributions of the movement pixel determined according to formula (9) and formula (10), and formula (11) and formula (12) decision are quiet
The only MAE feature distributions of pixel, it can be deduced that C=8, σgDistribution curve when=2, such as Fig. 3 A, and C=4, σgDistribution when=2
Curve, such as Fig. 3 B.
The σ when noise level is identical is can be seen that from Fig. 3 A and 3Bg=2, when contrast C is 8, move pixel and static
The MAE features of pixel do not have overlapping region substantially, have relatively good separability, and when contrast C is reduced to 4, motor image
Plain MAE features have had larger overlapping region with static pixel MAE features.
If contrast C and noise level σgRelation be
C=x σg (13)
X is Contrast-to-noise ratio, by substantial amounts of data analysis, works as x>3, i.e. contrast>During 3 times of noise levels, movement
The MAE features overlapping region of pixel and static pixel is smaller, has preferable separability, works as x<When 3, overlapping region is with x's
Reduce gradually increase, separability declines.
2nd, the adaptive video denoising system of contrast
As can be seen from the above analysis, when Contrast-to-noise ratio x is smaller, if carrying out movement inspection by threshold value
Survey, no matter how threshold value is chosen, and can all have detection mistake, if threshold value is smaller, it may occur that missing inspection, if threshold value is larger, can send out
Raw false-alarm, but phenomenon pair of the distortion phenomenon of the moving target hangover produced due to missing inspection than the noise that false-alarm produces without removal
Human eye vision is even more serious, so under low-contrast circumstances, in order to reach relatively good visual effect, motion detection should be use up
The generation of amount control missing inspection.For this reason, this patent provides contrast adaptive video denoising system to solve the above problems.
2, contrast adaptive video denoising system explanation
The contrast adaptive video denoising system of the present embodiment is as shown in Figure 4.The system includes：Deposited including frame, interframe
Difference characteristic computing module, contrast computing module, low contrast regions detection module, motion detection block, Motion Adaptive
Time-domain filtering module；Frame is deposited for caching filter result；Frame difference feature calculation module is according to video present incoming frame and frame
Previous filtering frame in depositing calculates output interframe difference characteristic；Contrast computing module calculates output currently according to present incoming frame
The local contrast of input frame；Low contrast regions detection module calculates according to present incoming frame local contrast and exports low contrast
Spend Region confidence；Motion detection block is according to low contrast regions confidence level and the frame of frame difference feature calculation module output
Between difference calculate output pixel probability of motion；During Motion Adaptive time-domain filtering module is deposited according to video present incoming frame and frame
Previous filtering frame and the probability of motion of each pixel carry out Motion Adaptive time-domain filtering, finally export current filter frame
Deposit frame is deposited.
Frame difference feature calculation module calculates the frame difference of the previous filtering frame during video present incoming frame and frame are deposited
Feature, has many frame difference feature calculation methods to use, such as simple poor, absolute difference, absolute difference and SAD (Sum
Of Absolute Difference), average absolute value difference MAE (Mean Absolute Error).Used in the present embodiment
Frame difference is characterized as MAE features, as formula (8) defines.
As shown in Figure 5A, contrast computing module includes horizontal gradient computing unit, Grads threshold to contrast computing module
Computing unit, intermediate zone detection unit, left average calculation unit, right average calculation unit, absolute difference computing unit；
Horizontal gradient computing unit is used to present incoming frame being transformed to horizontal gradient image G；Grads threshold computing unit
Output Grads threshold Gt is calculated according to horizontal gradient image G；Intermediate zone detection unit is according to horizontal gradient image G, Grads threshold
Gt calculates the intermediate zone mark α for exporting measuring point pixel to be checked, and local window around measuring point pixel to be checked is divided into left window
And right window；Left average calculation unit calculates the output non-intermediate zone pixel of left window according to present incoming frame and intermediate zone mark α
Gray average left_mean；Right average calculation unit is non-according to present incoming frame and intermediate zone mark α calculating output right windows
The gray average right_mean of intermediate zone pixel；Absolute difference computing unit calculates the ash of the output non-intermediate zone pixel of left window
The absolute value of the difference of average left_mean and the gray average right_mean of the non-intermediate zone pixel of right window is spent as current
The local contrast C of input frame.
In order to facilitate the contrast computational methods for understanding this patent, now the corresponding method of the module is illustrated, is such as schemed
5B show through on a horizontal line of moving object and background intersection its row coordinate of pixel j (j represent pixel in water
Square upward coordinate) and pixel grey scale be worth corresponding diagram, the gray value of moving object is V1, and the gray value of the gray scale of background is
V2, there are certain intermediate zone between moving object and background.Then contrast C=V2-V1 of moving object and background.
In order to calculate C, V1 and V2 need to be estimated, it is specific as shown in Fig. 5 (C), if present incoming frame I's is to be detected
Point pixel (i, j) contrast calculation window is (2N+1) x1, and pixel coordinate is expressed as (i, j+n) in window, the picture of-N≤n≤0
Element is the pixel of left window, and the pixel of 1≤n≤N is the pixel of right window；Then estimated using the average of the pixel in left window
V1, estimates V2 using the average of the pixel in right window, and due to left and right window, there is certain intermediate zone, these mistakes
V1 can be influenced by crossing the gray scale of the pixel of band, and the correct estimation of V2, can more connect so calculating average after intermediate zone pixel is removed
The right value of nearly V1 and V2.The specific calculation procedure of the local contrast C of present incoming frame I is calculated in contrast computing module
For：
Step 11, calculated level gradient image G
Horizontal gradient can be calculated with gradient operator and image convolution, the present embodiment is calculated using the Sobel gradients of 3x3
Son.
Step 12 calculates Grads threshold Gt
Calculating Grads threshold Gt methods in point position (i, j) to be checked is：Take and taken around measuring point pixel (i, j) to be checked
Local (2N+1) x1 windows, calculate the maximum of gradients max_grad and Grads threshold Gt of local window, such as formula (14) and (15)
It is shown.
Max_grad (i, j)=maxj-N≤n≤j+NG (i, n) (14)
Gt (i, j)=W*max_grad (i, j) (15)
Wherein, W is the proportionality coefficient that Grads threshold is maximum of gradients, and W, which can use, does 0.7.
Step 13, using horizontal gradient image G, Grads threshold Gt, intermediate zone detection is carried out, calculates measuring point pixel to be checked
The non-intermediate zone mark α of (2N+1) the x1 local windows of (i, j)
I.e. when the gradient G of the pixel in local window is less than Grads threshold Gt, which is non-intermediate zone pixel.
Step 14, the gray average of the non-intermediate zone pixel of left window is calculated using preceding input frame I, and non-intermediate zone mark α
left_mean
In the local window of (2N+1) x1 around pixel (i, j) to be detected, coordinate is (i, j+n) and-N≤n≤0
Pixel is the pixel of left window.The gray average left_mean of the non-intermediate zone pixel of left window is calculated, specific formula is formula
(17)、(18)、(19)。
Left_mean (i, j)=left_sum (i, j)/left_count (i, j) (19)
Left_sum in formula (17) is the sum of the gray value of non-intermediate zone pixel in left window, the left_ in formula (18)
Count is the number of non-intermediate zone pixel in left window, and formula (19) calculates the gray average of non-intermediate zone pixel.
Step 15, the gray average of the non-intermediate zone pixel of right window is calculated using image I, and non-intermediate zone mark α
In the local window of (2N+1) x1 around pixel (i, j) to be detected, coordinate is (i, j+n) and 1≤n≤N
Pixel is the pixel of right window.The calculating such as formula (20) of the gray average right_mean of the non-intermediate zone pixel of right window, (21),
(22) shown in, the right_sum in formula (20) is the sum of the gray value of non-intermediate zone pixel in right window.In formula (21)
Right_count is the number of non-intermediate zone pixel in left window.Formula (22) calculates the gray scale of the non-intermediate zone pixel of right window
Average.
Right_mean (i, j)=right_sum (i, j)/right_count (i, j) (22)
Step 16, the gray average left_mean and the non-intermediate zone pixel of right window of the non-intermediate zone pixel of left window are utilized
Gray average right_mean calculate contrast C, as shown in formula (23)
C (i, j)=| right_mean (i, j)-left_mean (i, j) | (23)
Low contrast regions detection module receives the contrast C input of contrast module calculating, calculates low contrast area
Domain confidence level R_LC.Carry out low contrast regions detection purpose be, can be in low contrast district using traditional method for testing motion
Domain produces undetected error, produces the distortion phenomenon of moving target hangover.It can be seen that from Fig. 3 A and 3B when noise level is identical
(σg=2) when, contrast C is 8, the MAE features for moving pixel and static pixel do not have overlapping region substantially, can use tradition
The adaptive method for testing motion of noise take 2 times or 3 times of noise levels be motion detection threshold, without bringing detection mistake
Difference.And when contrast C is reduced to 4, movement pixel MAE features have had larger overlapping with static pixel MAE features
Region, if detected using 2 times of traditional noise levels, can cause substantial amounts of undetected error.This patent provides will be low right
The method come out than degree region detection.It can be detected after detecting with the contrast adaptive motion of this patent to avoid leak
The generation of error detection by mistake.
The low contrast regions detection method that the present embodiment uses is not detected merely with contrast, also utilizes noise water
Put down to detect, so being a kind of adaptive low contrast regions detection method of noise.Same contrast, in low noise
When and strong noise when different influences can be brought to motion detection, it is good using the adaptive detection method of noise level
Place can eliminate the influence of noise.As shown in Figure 6 A and 6 B, in contrast C=4, when noise level is 1, static pixel and fortune
There is no overlapping region between dynamic pixel, but under same contrast, when noise level is 2, as shown in Figure 6B, static pixel and
There is overlapping region between movement pixel, if detected using 2 times of traditional noise levels, substantial amounts of missing inspection can be caused wrong
By mistake.
The adaptive low specific computational methods than degree region detection of noise
Step 21, Contrast-to-noise ratio X is calculated, as shown in formula (25)
Step 22, low contrast regions confidence level R_LC is calculated according to Contrast-to-noise ratio X.Fig. 7 A, 7B show two kinds
Calculate the curve of R_LC.Contrasted zones and high-contrast area threshold limit value X1, low confidence drop threshold X2 are set, wherein
X2 ＜ X1, as X (i, j)≤X2, confidence level R_LC is 1, and as X2 ＜ X (i, j) ＜ X1, confidence level R_LC is with Contrast-to-noise ratio
The increase of X (i, j) is by 1 to 0 monotone decreasing, and confidence level R_LC is 0 during X1≤X (i, j).
Fig. 7 C show another curve for calculating R_LC, eliminate the influence of smooth area, specially set threshold X 1,
X2, X3, X4, wherein X4 ＜ X3 ＜ X2 ＜ X1, as X (i, j)≤X4, confidence level R_LC is 0, is put as X4 ＜ X (i, j) ＜ X3
Reliability R_LC is with the increase of Contrast-to-noise ratio X by 0 to 1 monotonic increase, and confidence level R_LC is 1 during X3≤X (i, j)≤X2, when
Confidence level with the increase of R_LC Contrast-to-noise ratios X by 1 to 0 monotone decreasing, put by when X1≤X (i, j) during X2 ＜ X (i, j) ＜ X1
Reliability is 0.
Motion detection block receive frame difference feature calculation frame difference feature m and low contrast regions detection it is low
Contrast district confidence level R_LC is inputted, and is carried out the adaptive motion detection of contrast, is exported probability of motion.In low contrast confidence
The higher region of degree, moving between pixel and static pixel has larger overlapping region, at this time, the fortune brought due to undetected error
The distortion phenomenon of moving-target hangover is even more serious than the phenomenon that the noise that false-alarm error tape comes does not remove completely, so in low contrast
Region is spent, this patent is adjusted the parameter of motion detection with low contrast regions confidence level, the hair of undetected error is controlled with this
It is raw.
If the output of motion detection shows a kind of method of motion detection for probability of motion R_Motion, Fig. 8, wherein
T1, T2 are the soft-threshold for carrying out motion detection,：As m (i, j) ＜ T1, the probability of motion R_Motion of pixel is 1, as T1≤m
During (i, j)≤T2 the probability of motion R_Motion of pixel with the increase of frame difference feature m by 1 to 0 monotone decreasing, as T2 ＜ m
The probability of motion R_Motion of pixel is 0 when (i, j).
The parameter of motion detection is adjusted using low contrast regions confidence level, in low contrast regions, will be moved
The parameter adjustment of detection is to more encouraging pixel to be detected as movement pixel, so as to reduce omission factor.If examined using the movement of Fig. 8
Survey method, method of adjustment is reduces movement inspection threshold value, as shown in formula (26), (27)
T1 (i, j)=(1- α R_LC (i, j)) * T1Preset (26)
T2 (i, j)=(1- β R_LC (i, j)) * T2Preset (27)
T1PresetAnd T2PresetFor default motion detection parameters, can be set according to traditional method for testing motion, α
It is the parameter that is previously set with β.It can be set to α=0.5, β=0.5.Then in high-contrast area, low contrast confidence level R_
LC (i, j)=0, then T1 (i, j)=T1Preset, T2 (i, j)=T2Preset, traditional method for testing motion can be deteriorated to, is protected
Card high-contrast area had not both had false-alarm mistake, also without undetected error, so as to ensure denoising effect, and in low contrast regions,
Low contrast confidence level R_LC (i, j)=0, then T1 (i, j)=(1- α) * T1Preset, T2 (i, j)=(1- β) * T2Preset, threshold value
Reduce, so as to so that the generation of control undetected error.
Motion Adaptive time-domain filtering module receives the probability of motion R_Motion inputs of motion detection block calculating, and
Present incoming frame image and frame in depositing it is previous filtering frame input, with probability of motion instruct present image and it is previous filtering frame plus
Power filtering.The big pixel of probability of motion does not use time-domain Weighting Filter, and the small pixel of probability of motion takes time-domain Weighting Filter.From
And while stagnant zone denoising is reached, moving target hangover and time domain blooming will not be produced in moving region.
Claims (10)
1. a kind of adaptive video denoising system of contrast, including frame is deposited, frame difference feature calculation module, motion detection mould
Block, Motion Adaptive time-domain filtering module, it is characterised in that further include contrast computing module, low contrast regions detection mould
Block；Contrast computing module calculates the local contrast C of output present incoming frame I according to present incoming frame I；Low contrast regions
Detection module calculates output low contrast regions confidence level R_LC according to the local contrast C of present incoming frame I；Motion detection mould
Block calculates output pixel according to low contrast regions confidence level R_LC and the frame difference of frame difference feature calculation module output
Probability of motion R_Motion, wherein, the contrast computing module includes horizontal gradient computing unit, Grads threshold calculate it is single
Member, intermediate zone detection unit, left average calculation unit, right average calculation unit, absolute difference computing unit；
Horizontal gradient computing unit is used to present incoming frame being transformed to horizontal gradient image G；Grads threshold computing unit foundation
Horizontal gradient image G calculates output Grads threshold Gt；Intermediate zone detection unit is according to horizontal gradient image G, Grads threshold Gt meter
The intermediate zone mark α for exporting measuring point pixel to be checked is calculated, and local window around measuring point pixel to be checked is divided into left window and the right side
Window；Left average calculation unit calculates the ash of the output non-intermediate zone pixel of left window according to present incoming frame and intermediate zone mark α
Spend average left_mean；Right average calculation unit calculates the output non-transition of right window according to present incoming frame and intermediate zone mark α
Gray average right_mean with pixel；The gray scale that absolute difference computing unit calculates the output non-intermediate zone pixel of left window is equal
The absolute value of the difference of value left_mean and the gray average right_mean of the non-intermediate zone pixel of right window is as current input
The local contrast C of frame I.
2. a kind of adaptive video denoising system of contrast as claimed in claim 1, it is characterised in that horizontal gradient calculates single
Member uses gradient operator or image convolution calculated level gradient image G.
A kind of 3. adaptive video denoising system of contrast as claimed in claim 2, it is characterised in that the meter of Grads threshold Gt
Calculation method is：Part (2N+1) × l windows are taken around measuring point pixel (i, j) to be checked, the gradient for calculating local window is maximum
Value max_grad, formula are
Max_grad (i, j)=maxj-N≤n≤j+NG (i, n)
And then Grads threshold Gt is calculated, its formula is
Gt (i, j)=W*max_grad (i, j)
Wherein, W is the proportionality coefficient that Grads threshold is maximum of gradients, and (2N+1) × l windows, are that specification is (2N+1) × l
Local window, maxj-N≤n≤j+NG (i, n) is in part (2N+1) × l windows around measuring point pixel (i, j) to be checked, and row are sat
Maximum Grad when target scope changes between [j-N, j+N].
4. a kind of adaptive video denoising system of contrast as claimed in claim 3, it is characterised in that calculate measuring point picture to be checked
The formula of the non-intermediate zone mark α of (2N+1) × l local windows of plain (i, j) is
A kind of 5. adaptive video denoising system of contrast as claimed in claim 4, it is characterised in that the non-intermediate zone of left window
The computational methods of the gray average right_mean of the pixel of the non-intermediate zone of gray average left_mean and right window of pixel
For：
In (2N+1) × l local windows of the measuring point pixel (i, j) to be checked of present incoming frame I, pixel coordinate is expressed as in window
(i, j+n), the pixel of-N≤n≤0 are the pixel of left window, and the pixel of 1≤n≤N is the pixel of right window；
The calculation formula of the gray average left_mean of the non-intermediate zone pixel of left window is：
Left_mean (i, j)=left_sum (i, j)/left_count (i, j)
Wherein αnFor the non-intermediate zone mark of nth pixel, left_sum is the gray value of non-intermediate zone pixel in left window
With the number that, left_count is non-intermediate zone pixel in left window；
The calculation formula of the gray average right_mean of the non-intermediate zone pixel of right window is：
Right_mean (i, j)=right_sum (i, j)/right_count (i, j)
Wherein, right_sum is the sum of the gray value of non-intermediate zone pixel in right window, and right_count is non-in right window
The number of intermediate zone pixel, I (i, j+n) they are the gray value of the measuring point pixel (i, j+n) to be checked of present incoming frame I, wherein, n exists
Change between [- N, N].
A kind of 6. adaptive video denoising system of contrast as claimed in claim 5, it is characterised in that the low contrast
Region detection module calculates low contrast regions confidence level R_LC according to Contrast-to-noise ratio X and calculates low contrast regions confidence
Degree, the calculation formula of Contrast-to-noise ratio X are
Wherein, C (i, j) be pixel contrast, σgFor noise level.
7. a kind of adaptive video denoising system of contrast as claimed in claim 6, it is characterised in that according to contrast noise
The method that low contrast regions confidence level R_LC is calculated than X is：Set contrasted zones and high-contrast area threshold limit value X1,
Low confidence drop threshold X2, wherein X2 ＜ X1, as X (i, j)=X2, confidence level is 1, the confidence as X2 ＜ X (i, j) ＜ X1
Degree is with the increase of Contrast-to-noise ratio X (i, j) by 1 to 0 monotone decreasing, and confidence level is 0 during X1≤X (i, j).
8. a kind of adaptive video denoising system of contrast as claimed in claim 6, it is characterised in that according to contrast noise
The method that low contrast regions confidence level R_LC is calculated than X is：Threshold X 1, wherein X2, X3, X4, X4 ＜ X3 ＜ X2 ＜ X1 are set,
As X (i, j)≤X4, confidence level is 0, and as X4 ＜ X (i, j) ＜ X3, confidence level is single by 0 to 1 with the increase of Contrast-to-noise ratio X
Adjust and be incremented by, confidence level is 1 during X3≤X (i, j)≤X2, as X2 ＜ X (i, j) ＜ X1 confidence level with Contrast-to-noise ratio X increasing
Greatly by 1 to 0 monotone decreasing, confidence level is 0 during X1≤X (i, j).
9. a kind of as adaptive video denoising system such as contrast any one of claim 1-8, it is characterised in that described
Motion detection block calculate output pixel probability of motion, its computational methods is soft-threshold method for testing motion, is specifically included
Following steps：
Step 11, the soft-threshold of motion detection is set as T1, T2,
T1 (i, j)=(1- α R_LC (i, j)) * T1Preset
T2 (i, j)=(1- β R_LC (i, j)) * T2Preset
Wherein, α and β be setting preset parameter, T1PresetAnd T2PresetFor default motion detection parameters；
Step 12, the probability of motion of pixel is calculated according to frame difference feature m：As m (i, j) ＜ T1, the probability of motion of pixel is
1, as T1≤m (i, j)≤T2 the probability of motion of pixel with the increase of frame difference feature m by 1 to 0 monotone decreasing, as T2 ＜ m
The probability of motion of pixel is 0 when (i, j).
10. a kind of adaptive video denoising system of contrast as claimed in claim 9, it is characterised in that the movement is certainly
Adapt to time-domain filtering module to be filtered present incoming frame using the time-domain filtering method of Motion Adaptive, be specially：Setting
Probability of motion threshold value Q；According to probability of motion R_Motion instruct present incoming frame and frame deposit in the time domain of previous filtering frame add
Power filtering：Time-domain Weighting Filter is carried out as R_Motion≤Q, as Q ＜ R_Motion without time-domain Weighting Filter.
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