WO2019132091A1

WO2019132091A1 - Method for removing noise from up-scaled video by machine learning-based dynamic parameters

Info

Publication number: WO2019132091A1
Application number: PCT/KR2018/000086
Authority: WO
Inventors: 이진학; 황부군; 김성수
Original assignee: 주식회사 케이블티비브이오디
Priority date: 2017-12-28
Filing date: 2018-01-03
Publication date: 2019-07-04
Also published as: KR101987079B1

Abstract

A method for removing noise of up-scaled video by machine learning-based dynamic parameters according to the present invention comprises: an input step of inputting an object image from which noise is to be removed; a target selection step of selecting a current frame image from which the noise is to be removed, among the frame images constituting the object image, P (where P is a natural number greater than or equal to 1) preceding frame images which precede the current frame image, and Q (where Q is a natural number greater than or equal to 1) following frame images which follow the current frame image; a matching step of performing a motion estimation processing on the basis of a common object, and depending on the result thereof, creating a compensated frame image in which the P preceding frame images and the Q following frame images are motion-matched on the basis of the current frame image; a parameter calculation step of calculating a weight parameter (ai) for each frame by using the compensated frame image and the current frame image; and an operation processing step of removing the noise of the current frame image.

Description

Method of removing noise of up-scaled video by using dynamic learning-based dynamic parameters

More particularly, the present invention relates to a method and apparatus for removing noise from a video image, and more particularly, to a method and apparatus for noise reduction, And more particularly to a noise cancellation method that can be optimized for upscaled motion pictures by dynamically determining the weight parameters required for the upscaled motion picture.

In addition to display technology, hardware technology of various video devices including video panels has dramatically developed, and home TV is also becoming larger, so that demand for high-definition contents is being amplified. This tendency can be found in the gradual expansion of UHDTV (Ultra High-Definition Television) sales and commercialization of UHD broadcasting.

However, since UHD contents require considerable cost investment in comparison with existing HD contents, content produced by the UHD is not universally activated yet. Therefore, terrestrial, IPTV, cable TV industry, etc., (UHD) video contents by applying image processing technology such as upscaling and so on.

As a technology for image upscaling, various interpolation methods for interpolating pixel values are applied as disclosed in Korean Laid-Open Publication No. 10-2009-0039830 (Apr. 22, 2009), and high frequency components are extracted from an input image, A technique of generating a high-resolution image having a resolution higher than that of an input image through a method of fusing an image and a high-frequency image may be used.

However, in the process of up-scaling an HD-class image to a UHD-class image, various noise (ringing artifact, alias artifact, jaggy artifact, thermal noise) There is a problem that the influence due to noise is further exacerbated in the image upscaled from the image. Thus, noise removal in the up-scaling process may become more important than noise removal in the general image.

As a method of removing noise from an image, a temporal moving average filter, which is a kind of time domain filter such as the following conversion equation using pixel values of a previous frame, is mainly used. A method of removing noise using this conversion formula is disclosed in Korean Unexamined Patent Application Publication No. 10-2007-0089485 (Aug. 31, 2007).

In this conversion equation, the parameter a is a weight parameter that indicates the temporal correlation coefficient of the image. As the coefficient increases, the influence of the previous frame becomes larger and the noise removal effect becomes larger. However, There is a new problem that many residual images are generated.

In order to solve such a problem, as disclosed in Korean Patent Registration No. 10-1558532 (Oct. 20, 2015), the parameter a value is adjusted according to the noise level in the time domain filter to minimize the influence of the afterimage of the previous frame, Techniques for compensating for image degradation due to noise by applying a noise level to the measurement are also presented.

However, this technique has a problem that the efficiency of noise removal is extremely lowered in a high-quality image such as a UHD, in particular, only a parameter determination method is simple and only an edge effect can be exhibited to some extent.

[Prior Art Literature]

[Patent Literature]

(Patent Document 1) Korean Laid-Open Patent Publication No. 10-2009-0039830 (Apr. 22, 2009)

(Patent Document 2) Korean Published Patent Application No. 10-2007-0089485 (Aug. 31, 2007)

(Patent Document 3) Korean Patent Registration No. 10-1558532 (Oct. 20, 2015)

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above problems, and it is an object of the present invention to provide a method and apparatus for dynamically determining a weight parameter of a time domain filter that can be optimized for a frame image based on a machine learning technique, (UHD) image from a high-resolution image (HD image) to a high-resolution image (UHD) image, and a recording medium on which the method is implemented.

Other objects and advantages of the present invention will become apparent from the following description, and it will be apparent from the description of the embodiments of the present invention. Further, the objects and advantages of the present invention can be realized by a combination of the constitution shown in the claims and the constitution thereof.

According to another aspect of the present invention, there is provided a method of removing noise of an up-scaled moving image by using a dynamic learning-based dynamic parameter, the method comprising: inputting an object image, which is a noise- A P (P is a natural number equal to or larger than 1) preceding frame image preceding the current frame image, and a Q (1) preceding the current frame image, based on the current frame image, (Q is a natural number equal to or greater than 1) neighboring frames; A matching step of performing motion estimation processing on the basis of a common object and generating a compensated frame image in which the P preceding frame images and Q following frame images are motion matched based on the current frame image, Using the compensation frame image and the current frame image,

A parameter calculating step of calculating a parameter; And an arithmetic processing step of removing noise of the current frame image according to the following equation.

In the above formula

A current frame image from which noises have been removed,

A current frame image including noise,

The compensation frame image.

The present invention may further include a block dividing step of dividing each of the current frame image and the compensated frame image into units of a first reference block. In this case, The current frame image and the compensated frame image are used to calculate a weight parameter for each frame

), And the arithmetic processing step may be configured to remove noise of a target area forming a first reference block of the current frame image.

In addition, the parameter calculation step of the present invention may include calculating a parameter having a size smaller than a first reference block unit for a current frame image and a compensated frame image, each of which is divided in units of the first reference block through learning about a noise image and a noise- A first CNN processing step based on a convolution neural network (CNN) for calculating a value block; (CNN) based convolutional neural network (CNN) for calculating a feature value block hierarchically with respect to a feature value block calculated in the first CNN processing step until a final 1x1 size feature value block is outputted as a result CNN processing step; And the value of the final 1x1 size feature value block is divided into a weight parameter for each frame

), And calculating a result of the calculation.

In addition, the present invention may further comprise an upscaling step of performing upscaling of the noise-removed image from the arithmetic processing step and outputting the upscaled image.

The present invention may further include a post-processing step of removing the subsequent noise from the up-scaled image, wherein the post-processing step of the present invention includes: a second input step of inputting the up-scaling image; A second current frame image to be subjected to noise removal among the frame images constituting the up-scaling image, a second preceding frame image P (P is a natural number of 1 or more) preceding the second current frame image, A second object selecting step of selecting a second trailing frame image of Q (Q is a natural number of 1 or more) trailing based on the current frame image; A motion compensation processing unit for performing motion estimation processing on the basis of the common object, and for outputting the P second preceding frame image and the Q second rear frame image in accordance with a result of the motion estimation processing, A second matching step of generating an image; The second compensating frame image and the second current frame image are used to calculate a second weighting parameter for each frame

A second parameter calculating step of calculating a second parameter; And a second arithmetic processing step of removing noise of the second current frame image according to the following equation.

In the above formula

A second current frame image from which noises have been removed,

A second current frame image including noise,

Is a second compensation frame image.

The present invention may further include a second block dividing step of dividing each of the second current frame image and the second compensation frame image into a first reference block unit. In this case, the second parameter calculating step Using a second current frame image and a second compensation frame image that are respectively divided into the first reference block unit and the second weighting parameter for each frame

And the second arithmetic processing step may be configured to remove noise of a target area forming a first reference block of the second current frame image.

Here, the second parameter calculation step of the present invention may include calculating a first reference value for a second current frame image and a second compensation frame image, each of which is divided in units of the first reference block through learning about a noise image and a noise- A second CNN processing step based on a Convolution Neural Network (CNN) for calculating a feature value block having a size smaller than a block unit; A CNN (Convolution Neural Network) -based apparatus for calculating a feature value block hierarchically on a feature value block calculated in a second CNN processing step until a final 1x1 feature value block is outputted as a result 2 subsequent CNN processing step; And a value of the final 1x1 size feature value block is divided into a second weight parameter for each frame

And a second result calculating step of calculating the second result.

More preferably, the second matching step of the present invention is configured to perform motion estimation processing using the motion estimation processing result performed in the matching step and the estimated area determined by the upscaled multiple information in the upscaling step .

The noise removal method according to the present invention eliminates the method of determining the weight parameter of the time domain filter by the fixed value and removes the weight parameter dynamically determined using the hierarchical application of the machine learning technique, By applying differentially for each image, it is possible to provide an effect of optimizing the noise reduction of an upscaled moving picture / video.

In addition, according to another preferred embodiment of the present invention, it is possible to perform the elimination processing of the first noise in the pre-processing before the up-scaling processing of the image to be subjected to the noise removal, By performing the noise canceling processing, it is possible to perform the noise canceling of the upscaled moving picture more effectively.

Further, according to another preferred embodiment of the present invention, the result of the motion estimation and motion compensation (registration movement) processing used in the preprocessing process is applied cyclically in the post- It is possible to provide an effect of further maximizing the efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate preferred embodiments of the invention and, together with the detailed description of the invention given below, serve to better understand the technical idea of the invention, And shall not be construed as limited to such matters.

FIG. 1 is a block diagram showing the configuration of a noise elimination device with a preprocessing unit as a center in accordance with a preferred embodiment of the present invention. FIG.

FIG. 2 is a block diagram showing a configuration of a noise elimination apparatus, which is mainly composed of a post-processing unit according to a preferred embodiment of the present invention;

3 is a view for explaining motion estimation and motion compensation or registration movement used in the noise removal method of the present invention,

FIG. 4 is a block diagram showing a detailed configuration of a parameter calculating unit according to an embodiment of the present invention shown in FIG. 1;

FIG. 5 is a flowchart illustrating processing for implementing a noise removal method in a preprocessing process according to an exemplary embodiment of the present invention;

FIG. 6 is a flowchart illustrating a processing procedure for dynamically determining weight parameters using machine learning according to a preferred embodiment of the present invention. FIG.

FIG. 7 is a flowchart illustrating a process of removing noise based on a post-process according to an exemplary embodiment of the present invention. FIG.

8 is a diagram showing the basic structure of machine learning.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. Prior to this, terms and words used in the present specification and claims should not be construed as limited to ordinary or dictionary terms, and the inventor should appropriately interpret the concepts of the terms appropriately It should be interpreted in accordance with the meaning and concept consistent with the technical idea of the present invention based on the principle that it can be defined.

Therefore, the embodiments described in the present specification and the configurations shown in the drawings are merely the most preferred embodiments of the present invention and are not intended to represent all of the technical ideas of the present invention. Therefore, various equivalents It should be understood that water and variations may be present.

1 is a block diagram showing a detailed configuration of a noise removing apparatus 1000 in which a noise canceling method (to be referred to as 'noise removing method' hereinafter) of an up-scaled moving image by a dynamic learning- .

The noise reduction method according to the present invention can be implemented by a method in which the noise reduction method is installed and operated in the form of software in a hardware device such as a computer in which hardware resources such as a ROM, a RAM, an input / output module, a communication module, It is needless to say that the present invention can also be implemented by a dedicated terminal device in which a system on chip (SOC) implemented with a noise reduction method according to the present invention and other hardware configurations are combined.

Since the noise removing apparatus 1000 according to the present invention represents a terminal apparatus or a system implementing the noise removing method according to the present invention using the hardware resources, the noise removing method according to the present invention can be implemented or implemented It is to be understood that the present invention is not limited to the name of the apparatus, the terminal, or the system, but may be a noise removal apparatus 100 according to the present invention.

As shown in FIG. 1, the noise removing apparatus 1000 of the present invention includes a pre-processing unit 100, an up-scaling unit 200, and a post-processing unit 300. Hereinafter, the detailed configuration and detailed processing of the preprocessing unit 100 constituting the noise canceller 1000 of the present invention will be described with reference to FIG. 1 and FIG. 5 and the like, and details of the postprocessing unit 300 and the like The processing will be described later.

1, the preprocessing unit 100 of the present invention includes an input unit 110, an object selection unit 120, a matching unit 130, a block division unit 140, a parameter calculation unit 150, (160).

Prior to the detailed description of the present invention, the components of the noise removing apparatus 1000, the preprocessing unit 100, and the post-processing unit 300 shown in FIGS. 1 and 2 are not physically separated components It should be understood as a logically distinct component.

That is, since each constitution corresponds to a logical constituent element for realizing the technical idea of the present invention, even if each constituent element is constituted integrally or separately, if the function performed by the logical constitution of the present invention can be realized, It is to be understood that any component that performs the same or similar function should be interpreted as falling within the scope of the present invention regardless of the consistency of the name.

The input unit 110 of the present invention is configured to input an image to be noise canceled (hereinafter, referred to as an 'object image') and includes an interface function between another external apparatus and the noise removing apparatus 1000 of the present invention .

Since noise is scattered over a specific frame image (image) and its brightness is changed with time, noise has a characteristic of being conspicuous in the view of a viewer (user). Therefore, in order to effectively reflect such characteristics, The noise removal method basically employs a time domain filter (time domain function).

Since the time domain filter basically relies on the relationship between the preceding and following frame images based on the current frame image, the object selecting unit 120 of the present invention, when the object image is inputted through the input unit 110 of the present invention, A P previous frame image temporally preceding with respect to the current frame image, and Q trailing frame images trailing with respect to the current frame image are selected from the plurality of frame images constituting the current frame image (S500 in Fig. 5).

It should be understood that the current frame, the preceding frame, and the trailing frame have sequential timestamp properties so that the current frame in a particular processing can be a trailing frame or a preceding frame in previous or subsequent processing.

The number P of preceding frames and the number of trailing frames Q selected by the object selecting unit 120 of the present invention may be the same or different from each other as natural numbers of 1 or more, The number of pixels of the frame, the computation efficiency, the noise characteristic, the distribution, and the like.

When the current frame image, the P previous frame images, and the Q current frame images are selected as described above, the matching unit 130 of the present invention generates a motion vector or a motion (S510), and performs motion compensation or motion-matched processing on the P preceding frame images and Q following frame images based on the current frame image, (S520).

Each motion or motion-matched preceding or following frame image based on the current frame is referred to as a compensated frame image hereinafter for the sake of descriptive efficiency.

When objects and the like are matched in all frames of K (P (number of preceding frames) + (number of current frames) + Q (number of trailing frames)) through motion estimation and compensation (matching) Can be reduced to 1 / K by the following mathematical relation.

First, the motion-estimated image g (x, y) can be defined as the sum of noise-free image "f (x, y) and noise η (x, y) as follows.

[Equation 1]

&Quot; (2) "

In Equation 2,

(Including the current frame image) g (x, y) divided by K,

The

(X, y) as an average of the noise components, only the image f (x, y) without noise is averaged.

The

(X, y) as the variance of the motion estimation image K, and divides it by K, it represents the average noise size. Since it is assumed that f (x, y) is the same in K frames, only the η (x, y) is obtained by obtaining the variance, and since it is divided by K, the noise size is reduced to 1 / K.

In this way, noise removal can be effectively performed. However, if the image is complicated, the motion is complex, or the motion estimation is not accurately implemented, side effects such as residual image may occur.

Therefore, it is important to select the weight parameter effectively in the process of processing the preceding frame and the following frame in the time domain filter.

When the compensation frame (compensation frame image) is prepared as described above, the parameter calculation unit 150 of the present invention calculates the weight parameter for each current frame using the compensation frame image and the current frame image,

"(S540).

Weight parameter

The subscript "i" of the current frame means a corresponding order of the current frame to be subjected to the noise removal, and in order to overcome the conventional method having a fixed value, Lt; / RTI > The specific processing for calculating the weight parameter using the machine learning will be described later.

Finally, the operation processing unit 160 of the present invention removes the noise of the current frame image according to the following equation (S550). Equation (3) means time-domain filtering using the current frame (n-th frame), P preceding frames and Q (n) following frames as the time domain filter proposed by the present invention.

&Quot; (3) "

In Equation (3)

A current frame image from which noises have been removed,

(when i = 0) is a current frame image including noise,

(when i ≠ 0) means a compensation frame image.

More preferably, the noise reduction method according to the present invention may be configured to divide the current frame image into a plurality of reference block units and perform the noise removal processing on the divided blocks in order to further improve the accuracy of noise removal .

For this, the block division unit 140 of the present invention divides each of the current frame image and the compensated frame image (the P compensated (preceding) frame and the Q following frame) into a first reference block unit (S530). &Lt; / RTI >

It is a matter of course that the first reference block can have a variable size based on the efficiency of the operation processing, etc. since the noise removal accuracy is high as the unit of noise reduction is small, but the processing speed is slow. In the following description, the first reference block has an 8x8 size as an example.

If the current frame image and the compensated frame image are divided into the first reference block units, the parameter calculating unit 150 calculates the current frame image and the compensation frame, which are respectively divided into the first reference block (8 × 8) The weight parameter of the divided reference block unit of the current frame using the image

(S540), the arithmetic processing unit 160 removes noise of a target area forming an 8x8 reference block to be subject to noise removal processing in the current frame image (S550) .

The operation processing unit 160 of the present invention shifts the first reference block until the noise of all the target areas inside the first reference block is removed in step S560 Performs the above-described S540 to S560 processing cyclically.

Hereinafter, a processing procedure for determining a weight parameter according to a preferred embodiment of the present invention will be described in detail with reference to FIG. 4 and FIG.

In the present invention, in order to make the best use of the relationship with the surrounding values including the edge level and the edge direction, a weighting parameter for each reference block of K frames based on a machine learning

So that the blur phenomenon due to the temporal filtering of the motion image can be minimized.

Furthermore, in order to effectively perform motion noise cancellation by up-scaling, the up-scaling effect is further improved by performing noise removal before up-scaling and after up-scaling. The description is based on the weight parameter

Will be described later.

4, the parameter calculation unit 150 of the present invention may include a first CNN processing unit 151, one or more subsequent CNN processing units 153, and one or more ReLU units 152.

The order of each frame of the object image (low-resolution image) inputted through the input unit 110 is 1, 2, ... n, n + 1, ... (Current frame), the motion estimation and motion compensation (matching) processing is performed on the P preceding frames and the Q subsequent frames as described above .

Among them, it is assumed that the frame in which the current motion estimation and the motion compensation are performed is the n-mth frame. The subsequent process is performed in the same manner for the P preceding frames and the Q subsequent frames.

As described above, the motion between the n-th frame (current frame) and the n-m-th frame is estimated to calculate motion information for each pixel. The n-mth frame (n-mth compensation frame or compensation frame image) is compensated by moving the corresponding pixels by the calculated motion information using the calculated motion information of each pixel.

The motion compensated < RTI ID = 0.0 > nm <

And each 8x8 reference block is divided into 8x8 reference block units each having a corresponding weight parameter

Is used as a unit for determining

By using the 8x8 block as described above, the weight parameter

The noise is removed from the 4x4 (target area) block, which is the internal area of the 8x8 block. The noise cancellation unit may be 2x2 or 1x1 instead of 4x4, etc. The size of the noise cancellation unit can be variably applied depending on the trade-off relationship between the accuracy of noise cancellation and the computation processing speed to be.

A difference may occur in the value of the weight parameter of the neighboring target area block, and when the difference of the weight parameter is large, the block boundary may become prominent. Therefore, in order to effectively suppress the block boundary, 4 is made to overlap with the next target area block so that the noise can be removed.

The parameter calculation unit 150 of the present invention performs training using machine learning (deep learning) and calculates a weight parameter based on the training result using a convolution neural network (CNN) based first CNN unit And

subsequent CNN units

151 and 153 and an ReLU unit 152. [

The number of the CNN unit 151 and the subsequent CNN unit 153 may be variously set. However, in the description of the present invention, the CNN unit 151 and the ReLU unit 152 have three hidden layers, (151, 153) illustrate a structure having 3 × 3 CNN 2 parts, 4 × 4 CNN 1 parts and 1 × 1 CNN 1 parts, and each hidden layer has C1, C2 and C3 feature maps.

CNN is an application of the human brain structure and corresponds to modeling a CNN as a cluster of single CNNs, as if the human brain consisted of a minimum unit of neurons.

Neuron, which is a basic structure of deep learning, has an affine transform part for multiplying an input by a weight and calculating a sum as shown in FIG. 8, and an active function (Activation Function) part.

In the present invention, CNN is used as the affine transform part and ReLU (Rectified Linear Unit) is used as the active function part.

First, the first CNN unit 151 of the present invention learns a noise image and a noise-eliminated image to obtain a current frame image and a compensation frame image, which are respectively divided into a first reference block (8 × 8) 3CNN to calculate a feature value block (6 × 6) smaller than the first reference block (8 × 8) (S600).

That is, as shown in the right side of FIG. 6, a 3 × 3 CNN is performed on an 8 × 8 basic block to calculate and mapped one characteristic value, And 3 × 3 CNN are sequentially performed, thereby finally outputting 6 × 6 blocks.

The affine transform is performed on nine pixels of 3 × 3 blocks in the input 8 × 8 block, and the result is obtained by multiplying each pixel by weight and summing the weight. Then, the result is added to the first (leftmost Top) position. This process is performed by moving one pixel at a time to finally fill all the values of the 6x6 block (S600).

If only the luminance component (Y) data of the image is used, processing proceeds as described above. If the color space (R, G, B) data of the image is used, three R, G, B data to perform an affine transform on 3 × 3 × 3 (27) pixels. This process is performed by C1, which is the number of the first feature map.

After the first CNN is performed, ReLU is performed (S610). ReLU outputs 0 when the input is smaller than 0, and outputs the input as it is when the input is 0 or more.

When 6 × 6 blocks of C1 are input to the subsequent CNN unit 153 as a result of the first CNN unit 151 and the

ReLU unit

152, 3 × 3 CNN processing (S620) and ReLU are performed in the same manner as described above S630) C2 4x4 blocks are output.

Finally, when C2 4 × 4 blocks are inputted to the subsequent CNN unit 153 performing 4 × 4 CNN, an affine transform is performed on all the pixels of the C2 4 × 4 blocks (S640), and the ReLU is subsequently performed (S650) to generate C3 1 * 1 size feature maps.

Finally, the last succeeding CNN unit 153 performs 1 × 1 CNN on C3 inputs (S660), and finally calculates a weight parameter. As described above, the subsequent CNN unit 153 calculates the feature value block in a hierarchical manner with respect to the feature value block calculated by the first CNN processing unit 151 until the final 1x1 feature value block is outputted as a result , And the result calculation unit 155 of the present invention calculates a weighting parameter for the noise removal target region of the corresponding frame

).

The method of training the deep running structure of the present invention as described above can be configured as follows.

Produces multiple sample videos that artificially insert various noise into the original video. The inserted noise can be white Gaussian noise, Poisson noise, Salt & Pepper noise, coding noise, ringing artifact, alias artifact, jaggy artifact.

In order to improve the utility and versatility of machine learning, it is desirable to insert noise while changing the size of noise as well as the type of noise. For Training, prepare a pair of sample video with no noise for the same video and a target video without noise.

(8x8) set for the n-th frame and the motion compensated nm-th frame of the noise-embedded sample moving picture, respectively, and generates an n-th frame of the noise- (N + 1) -th frame (4 × 4 (target area) block) and noise-free n-th frame (n × 4) obtained by subtracting the noise from the obtained a value by using the reference block (8 × 8) The training is repeated so that the difference between the blocks is small.

The difference between the 4 × 4 block of the n-th frame from which the noise is removed and the 4 × 4 block of the n-th frame without noise is obtained by using the MSE (Mean Square Error) as follows.

&Quot; (4) "

In Equation (4), yi is the pixel of the 4x4 block of the nth frame with no noise,

Is the pixel of the 4x4 block of the n-th frame from which noise is removed, and d is the number of pixels in the block, which is 16 in this embodiment.

The training process is repeated until the MSE is at a minimum and no longer decreases.

Examples of hyper parameters for deep training training are: Optimization uses the Adam algorithm, the learning rate is 0.1, the momentum is 0.9, the weight decay is 0.0001, and the learning rate policy is "fixed." We use the "msra" constant "is used.

The weight values obtained through the above-described machine learning (deep running) training process are used in the diving structure to determine the weight parameters of the time domain filter.

7, S700). When the noise-canceled image is finally input from the operation processing unit 160 of the present invention, the up-scaling unit 200 of the present invention performs the up-scaling Processing is performed (S710) and finally the upscaled image is outputted.

Hereinafter, the post-processing unit 300 and the post-processing noise elimination processing S720 of the noise elimination apparatus 1000 of the present invention will be described in detail with reference to FIG. 2 and FIG.

2, the post-processing unit 300 of the present invention includes a second input unit 310, a second object selection unit 320, a second matching unit 320, A first block dividing unit 130, a second block dividing unit 140, a second parameter calculating unit 350, and a second calculation processing unit 360.

When the upscaled image is input from the upscaling unit 200 through the second input unit 310 of the post-processing unit 300, the second object selection unit 320 of the present invention generates a frame image (P is a natural number equal to or greater than 1) preceding the second current frame image and the second current frame image, which are subjected to the intermediate noise removal, Q (Q is a natural number of 1 or more) second rear frame images are selected (S721).

The second matching unit 330 performs motion estimation processing on the basis of the common object and outputs the P second preceding frame image and Q second following frame image as the second current frame image A second compensation frame image that is a motion-matched image is generated (S723).

At this time, the second matching unit 330 of the present invention has characteristics corresponding to both before and after the up-scaling of the motion of the common object, so that the processing such as motion estimation and compensation is not newly performed on the up- , And to perform motion estimation and compensation processing using the estimated region determined by the result of the motion estimation (motion vector and the like) performed in the matching unit 130 of the preprocessing unit 100 and the upscaled multiple information desirable.

For example, when the upscaling multiples of 2 are 3 and 4 times as much as the upscaling multiples of the motion estimation result performed by the preprocessing unit 110, The motion estimation processing may be performed using the determined range of the upper, lower, right, and left three pixels.

When the motion estimation and compensation processing is completed, the second block division unit 340 of the post-processing unit 300 according to the present invention divides each of the second current frame image and the second compensation frame image into a first reference block unit S723).

The second parameter calculation unit 350 of the present invention may use the second compensation frame image and the second current frame image that are divided in units of the first reference block through the same processing as the parameter calculation unit 150 described above The second weight parameter for each frame (

(Step S725).

Before the up-scaling, that is, the weight parameter

The weight parameter calculated in the post-process using the up-scaled image, etc.,

.

Then, the second operation processing unit 30 of the present invention cyclically applies the movement of the reference unit block S729 by using the following equation (5) and the method corresponding to the above-described method, (S728), the noise of the second current frame image is removed (S727).

&Quot; (5) "

In the above formula

A second current frame image from which noises have been removed,

A second current frame image including noise,

Is a second compensation frame image.

The second CNN unit 351, the ReLU unit 352 and the second succeeding CNN unit 353, which constitute the second parameter calculation unit 350 of the post-processing unit 300, Is the same as the parameter calculating unit 150 of the controller 100, detailed description thereof will be omitted.

In the second parameter calculation unit 350 of the post-processing unit 300, the second weight parameter

It is preferable to use an image in which the ratio of the upscaling of the original image is reversed and the downscale is performed. For example, downsampling is performed 1/2 times up-scaling, and down-scaling is performed 1/3 times up-scaling.

The above-described deep-running training is performed using noise-free moving pictures before downscaling and moving noise-containing moving pictures produced by performing up-scaling by incorporating various noise into the downscaled image.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not to be limited to the details thereof and that various changes and modifications will be apparent to those skilled in the art. And various modifications and variations are possible within the scope of the appended claims.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. It should be understood that various modifications may be made in the ordinary skill in the art.

The noise canceling method of the up-scaled moving image by the above-described machine learning based dynamic parameter of the present invention can be implemented as a computer-readable code on a computer-readable recording medium. The computer-readable recording medium includes all kinds of recording devices (CD-ROM, RAM, ROM, floppy disk, magnetic disk, hard disk, magneto-optical disk, etc.) in which data that can be read by a computer is stored. It also includes a server for transmission.

Claims

An input step of inputting an object image, which is a noise-canceled image;

A P (P is a natural number equal to or larger than 1) preceding frame image preceding the current frame image, and a Q (1) preceding the current frame image, based on the current frame image, (Q is a natural number equal to or greater than 1) neighboring frames;

A matching step of performing motion estimation processing on the basis of a common object and generating a compensated frame image in which the P preceding frame images and Q following frame images are motion matched based on the current frame image,

Using the compensation frame image and the current frame image,
A parameter calculating step of calculating a parameter; And

And removing the noise of the current frame image according to the following equation: < EMI ID = 14.0 >

In the above formula
A current frame image from which noises have been removed,
A current frame image including noise,
The compensation frame image.
The method according to claim 1,

Further comprising a block dividing step of dividing each of the current frame image and the compensation frame image into first reference block units,

Wherein the parameter calculating step calculates the weight parameter for each frame using the current frame image and the compensated frame image respectively divided in the first reference block unit
),

Wherein the operation processing step removes noise of a target area forming a first reference block of the current frame image, based on the machine learning based dynamic parameter.
3. The method according to claim 2,

A convolutional neural network for calculating a feature value block having a size smaller than a first reference block unit with respect to a current frame image and a compensated frame image respectively segmented by the first reference block unit through learning of a noise image and a noise- CNN, Convolution Neural Network) based first CNN processing step;

(CNN) based convolutional neural network (CNN) for calculating a feature value block hierarchically with respect to a feature value block calculated in the first CNN processing step until a final 1x1 size feature value block is outputted as a result CNN processing step; And

The value of the final 1x1 size feature value block is divided into a weight parameter for each frame
) Based on machine learning based dynamic parameters. &Lt; RTI ID = 0.0 > [10] < / RTI >
3. The method of claim 2,

Further comprising an up-scaling step of performing up-scaling of the noise-removed image when the noise-removed image is inputted from the arithmetic processing step and outputting the up-scaled image. Removal method.
5. The method of claim 4,

Further comprising a post-processing step of removing subsequent noise from the upscaled image,

The post-

A second input step in which the upscaling image is input;

A second current frame image to be subjected to noise removal among the frame images constituting the up-scaling image, a second preceding frame image P (P is a natural number of 1 or more) preceding the second current frame image, A second object selecting step of selecting a second trailing frame image of Q (Q is a natural number of 1 or more) trailing based on the current frame image;

A motion compensation processing unit for performing motion estimation processing on the basis of the common object, and for outputting the P second preceding frame image and the Q second rear frame image in accordance with a result of the motion estimation processing, A second matching step of generating an image;

The second compensating frame image and the second current frame image are used to calculate a second weighting parameter for each frame
A second parameter calculating step of calculating a second parameter; And

And removing a noise of the second current frame image according to the following equation: < EMI ID = 14.0 >

In the above formula
A second current frame image from which noises have been removed,
A second current frame image including noise,
Is a second compensation frame image.
6. The method of claim 5,

Further comprising a second block segmentation step of segmenting each of the second current frame image and the second compensation frame image into a first reference block unit,

Wherein the second parameter calculation step uses the second current frame image and the second compensation frame image that are each divided into the first reference block unit and the second weight parameter
),

Wherein the second computation processing step removes noise of a target area forming a first reference block of the second current frame image, based on machine learning based dynamic parameters.
7. The method according to claim 6,

A feature value block having a size smaller than that of the first reference block unit is calculated for the second current frame image and the second compensation frame image each segmented in the first reference block unit by learning about the noise image and the noise- A second CNN processing step based on a Convolution Neural Network (CNN);

A CNN (Convolution Neural Network) -based apparatus for calculating a feature value block hierarchically on a feature value block calculated in a second CNN processing step until a final 1x1 feature value block is outputted as a result 2 subsequent CNN processing step; And

The value of the final 1x1 size feature value block is divided into a second weight parameter for each frame
And a second result calculating step of calculating a moving speed of the moving image based on the motion-based dynamic parameter.
6. The method of claim 5, wherein the second matching step comprises:

And performing motion estimation processing using the motion estimation processing result performed in the matching step and the estimated area determined by the upscaled multiple information in the upscaling step. How to Remove Noise from Video.
A computer-readable recording medium on which a program for executing the method according to any one of claims 1 to 8 is recorded.