CN108648162B - Gradient-related TV factor image denoising and deblurring method based on noise level - Google Patents

Gradient-related TV factor image denoising and deblurring method based on noise level Download PDF

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CN108648162B
CN108648162B CN201810470422.3A CN201810470422A CN108648162B CN 108648162 B CN108648162 B CN 108648162B CN 201810470422 A CN201810470422 A CN 201810470422A CN 108648162 B CN108648162 B CN 108648162B
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冯华君
黄加紫
徐之海
李奇
陈跃庭
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Zhejiang University ZJU
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Abstract

The invention discloses a gradient correlation TV factor image denoising and deblurring method based on noise level. The images acquired by daily shooting are degraded to a certain degree, even if a static target is stably imaged, the acquired images also contain pixel-level fuzzy quantity, and noise is inevitable. The restoration operation of the noisy and fuzzy image can improve the image quality, however, contradiction exists between noise suppression and deblurring, and the balance of the noise suppression and the deblurring is required to be considered when the image is restored. The method provided by the invention provides an image de-noising and de-blurring method based on total variation regularization, a primary de-blurred image is obtained by combining the characteristics of a Richardson-Lucy algorithm while the image is subjected to fuzzy restoration, a plurality of regularization terms are used for constraining, the prior distribution characteristics of the gradient of a noise image are utilized, gradient-related distributed adjustment processing is carried out on a regularization weight factor, the noise can be effectively inhibited, the good image edge characteristics are kept, and the image with higher quality is obtained.

Description

Gradient-related TV factor image denoising and deblurring method based on noise level
Technical Field
The invention belongs to the field of digital image processing, and relates to a gradient-related TV factor image denoising and deblurring method based on noise level.
Background
With the popularization of photographing apparatuses and photographing techniques, photographing has become an important activity in daily life of people, and accordingly, the demand for image quality has been increasing. In the process of forming a natural image, the natural image may be affected by various aspects such as the motion of a target scene, the shake of an imaging device, the noise of an imaging sensor and the like, and finally a blurred and degraded image is obtained. In general, on the premise that the imaging system is linearly shifted unchanged, the process of forming a noisy and blurred image can be simply described as follows:
y(u,v)＝k(u,v)*x(u,v)+n(u,v)
wherein: k (u, v) is a point spread function (image blur kernel, PSF); is a convolution sign, x (u, v) represents a sharp image, and n (u, v) represents additive noise. Image blur is a common problem in image capturing, and even with stable imaging of a still object, there is still image blur of a small magnitude, and noise is difficult to avoid.
Fig. 1 shows daily shot images and a detailed enlarged view thereof.
Restoration processing of noisy and blurred images is a common approach. In the process of image restoration, a contradiction exists between deblurring and denoising, and balance is often difficult. In general image processing, a noise removal method for a noise image alone is not few, and a good effect can be achieved, but the high-frequency information of the image is sacrificed. The method is combined with the gradient distribution prior characteristic of the image with noise, is particularly applied to image restoration, and achieves a remarkable effect. The method provided by the invention belongs to the field of image non-blind restoration, and is characterized in that a preliminary deblurred image is obtained by combining the characteristics of a Richardson-Lucy algorithm while the image is subjected to fuzzy restoration, a plurality of regularization terms are used for constraining, and gradient-related distributed adjustment processing is carried out on regular weighting factors by utilizing the prior distribution characteristic of the gradient of a noise image, so that the noise can be effectively inhibited and good image edge characteristics can be kept.
Disclosure of Invention
Fig. 2 shows a comparison between a natural sharp image and a noisy sharp image and their respective gradient distribution characteristic statistical graphs, and the analysis can obtain the gradient distribution rule of a general noisy image: the gradient data is mainly distributed in a low gradient range, and a maximum value exists at a lower gradient position and shows unequal sparse distribution to two sides. The invention aims to obtain a primary restoration result by using different responses of noise gradients and applying an RL algorithm to obtain a blurred image, and apply the blurred image to the subsequent regularization variational method image restoration operation, and perform gradient-related distributed adjustment processing on regularization constraint factors by adding image constraint conditions, thereby finally achieving the restoration effect of inhibiting noise and keeping good image edge characteristics.
In order to achieve the above purpose, the invention adopts the following technical scheme: a gradient correlation TV factor image denoising and deblurring method based on noise level comprises the following steps:
(1) obtaining a preliminary deblurred image I by using Richardson-Lucy (RL) algorithm0；
(2) An energy equation model introduced into a traditional total variation regularization method is as follows:
Figure BDA0001663102500000021
wherein I is a potential sharp image, k is an image blurring kernel, and lambda is a constraint factor,
Figure BDA0001663102500000022
for the purpose of the first-order derivation operation,
Figure BDA0001663102500000023
for convolution operations, E (I) is the energy of image I;
(3) on the basis of the energy equation model in the step (2), introducing new variables w, v, theta, gamma and beta, and adding a gradient penalty term and a secondary gradient smoothing term to obtain the following optimization model:
Figure BDA0001663102500000024
Figure BDA0001663102500000025
wherein Ω is the image integration domain;
converting the continuous optimization model into a discrete model, wherein the solution of the discrete model relative to the image is as follows:
Figure BDA0001663102500000026
wherein, F and F-1Representing Fourier transform and inverse Fourier transform, F*Representing the complex conjugate operation, x, y represent the two integration directions of the image,
Figure BDA0001663102500000027
is a gradient operator;
meanwhile, the discrete model is decomposed into a w subproblem and a v subproblem:
problem with the w child:
Figure BDA0001663102500000028
v sub-problem:
Figure BDA0001663102500000029
(4) gradient penalty factor distributed processing, specifically:
(4.1) introduction of gradient-dependent TV penalty factor λsThe expression is as follows:
Figure BDA00016631025000000210
wherein the content of the first and second substances,
Figure BDA0001663102500000031
Figure BDA0001663102500000032
variable s determines lambdasIs the overall constraint strength of s ∈ [0,2 ]]；
Figure BDA0001663102500000033
A threshold value of the gradient is indicated,
Figure BDA0001663102500000034
the value is generally 0.04; parameter p1、b、d、p2κ together determining λsThe respective ranges are: p 1E [0,5 ]]，b∈[50,300]，d∈[0,20]，p2∈[-10,50]，κ∈[0,0.5]；
(4.2) by using λsReplacing a constraint factor lambda, and alternately solving a w subproblem, a v subproblem and a clear image I;
(5) and controlling beta and theta parameters in the solving process, and repeatedly and iteratively solving until a clear image I is obtained.
Further, a Richardson-Lucy algorithm is combined with a total variation regularization method, and the Richardson-Lucy algorithm acquires a preliminary deblurred image as a prior result of the total variation regularization method and applies the preliminary deblurred image to a subsequent restoration process.
Further, a gradient dependent TV penalty factor λ is introducedsThe factors considered are: the gradient data of the noisy image is mainly distributed in a low gradient range, and a maximum value exists at the low gradient position and is distributed unevenly and sparsely to two sides.
Further, in the step (3), the continuous optimization model is converted into a discrete model:
Figure BDA0001663102500000035
further, in the step (4.2), the solution of the w subproblem is:
Figure BDA0001663102500000036
Figure BDA0001663102500000037
the invention has the beneficial effects that: and acquiring a blurred image by using different responses of noise gradients and an RL algorithm to obtain a primary restoration result, applying the primary restoration result to subsequent regularization variational method image restoration operation, performing gradient-related distributed adjustment processing on regularization constraint factors by adding image constraint conditions, and finally acquiring a de-noised and de-blurred image. The method is suitable for processing a general noisy blurred image, effectively distinguishes image details and noise in a deblurring process according to the noise gradient characteristic, can effectively inhibit the noise while keeping good image edge characteristic, gives consideration to the deblurring and denoising effects, and can reduce time cost to a certain extent due to the introduction of an RL algorithm.
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Fig. 1 is a blurred image of daily shooting and a detail enlargement example thereof, in which (a) is a shot image and (b) is a detail enlargement image.
FIG. 2 is a noisy image and its gradient statistical chart, in which (a) is a clear image, (b) is an image result after adding a noise variance of 0.001 of mean value, (c) is a gradient distribution statistical characteristic chart of (a), and (d) is a gradient distribution statistical characteristic chart of (b).
FIG. 3 is a schematic view of the process of the present invention.
FIG. 4 is a schematic diagram showing the detailed process of the method of the present invention.
FIG. 5 shows the parameter set to p1＝0.3、b＝90、d＝12、κ＝0.03、p2＝50、s＝0.5、
Figure BDA0001663102500000041
Time-gradient dependent distributed factor λsThe numerical distribution of (c).
Fig. 6 is a comparison graph of restoration effect, wherein (a) is a blurred image with noise, (b) is an RL algorithm restoration result, (c) is an FTVD algorithm restoration result, and (d) is a restoration result of the method of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings and specific examples.
The method utilizes different responses of noise gradients, applies an RL algorithm to obtain a fuzzy image to obtain a preliminary restoration result, applies the preliminary restoration result to subsequent regularization variational method image restoration operation, and performs gradient-related distributed adjustment processing on regularization constraint factors by adding image constraint conditions, thereby finally achieving the restoration effect of inhibiting noise and keeping good image edge characteristics.
The process of the invention is shown in the attached figures 3 and 4, and mainly comprises the steps of obtaining a preliminary deblurred image by an RL algorithm, introducing an image regularization energy equation, introducing a new variable and solving an optimization equation, performing gradient penalty factor distributed processing, performing iterative computation and the like.
Step 1, acquiring a preliminary deblurred image by using a Richardson-Lucy (RL) algorithm. In general, the RL algorithm assumes that the image noise conforms to Poisson distribution, adopts the maximum likelihood method to estimate a clear image, and is an iterative recovery algorithm based on Bayesian analysis. For the poisson noise model, the likelihood probability of an image can be expressed as:
Figure BDA0001663102500000042
Figure BDA0001663102500000043
is a poisson process. The iterative formula of the RL algorithm can be obtained through derivation:
Figure BDA0001663102500000044
wherein, K*Is the adjoint of K, and t is the number of iterations. Although the RL algorithm is derived from the poisson noise model, it is equally applicable to other types of noise models. The preliminary sharper image obtained by the RL algorithm will be applied to the subsequent regularized variational model.
Step 2, introducing an energy equation model of a traditional total variation regularization method, wherein the energy equation model comprises the following steps:
Figure BDA0001663102500000045
in the above formula, I represents a latent image, I0Representing the image to be processed, h representing the image blur kernel, λxIn order to be a constraint factor, the method comprises the following steps,
Figure BDA0001663102500000051
for the purpose of the first-order derivation operation,
Figure BDA0001663102500000052
is a convolution operation.
Step 3, introducing new variables and solving an optimization equation, wherein the method comprises the following steps:
3-1, introducing new variables w, v, theta and gamma on the basis of a traditional energy equation model, adding a gradient penalty term and a secondary gradient smoothing term, and having the following model:
Figure BDA0001663102500000053
Figure BDA0001663102500000054
wherein Ω is the image integration domain; when β approaches 0 and θ approaches 0, the solution of the above model converges to the solution of the model (3).
The 3-2 model (4) is a continuous total variation model which can be converted into a discrete model as follows:
Figure BDA0001663102500000055
the above formula is an optimization function of I, w, v, which has a closed form of the optimal solution with respect to I, and can be obtained equivalently after Fourier transform:
Figure BDA0001663102500000056
wherein, F and F-1Representing Fourier transform and inverse Fourier transform, F*Represents a complex conjugation operation;
3-3 regarding the solution of w and v, since w and v are independent in the respective functions, their values can be solved sequentially by an alternating minimization method, thereby solving the problem into two w sub-problems and v sub-problems. When the w subproblem is processed, v takes a fixed value and is converted into a quadratic problem with solving independent variable w, and the solving formula is as follows:
Figure BDA0001663102500000057
when the v subproblem is processed, w takes a fixed value and is converted into a minimum value solving problem with an independent variable of v:
Figure BDA0001663102500000058
step 4, gradient penalty factor lambdasDistributed processing, comprising the steps of:
4-1 was investigated to conclude the following: gradient data of the noisy and clear image are mainly distributed in a low gradient range, and a maximum value exists at a lower gradient position and is distributed unevenly and sparsely to two sides. Applying this conclusion to a specific restoration process, taking into account that the noise constraints for different gradient values should be changed, and at the same time, not making too much limitation on useful edge information of the image, a gradient correlation factor constraint is introduced, in this case:
Figure BDA0001663102500000061
wherein the content of the first and second substances,
Figure BDA0001663102500000062
variable s determines lambdasIs the overall constraint strength of s ∈ [0,2 ]]；
Figure BDA0001663102500000063
A threshold value of the gradient is indicated,
Figure BDA0001663102500000064
the value is generally 0.04; parameter p1、b、d、p2κ together determining λsThe respective ranges are: p 1E [0,5 ]]，b∈[50,300]，d∈[0,20]，p2∈[-10,50]，κ∈[0,0.5](ii) a FIG. 5 shows p1＝0.3、b＝90、d＝12、κ＝0.03、p2＝50、s＝0.5、
Figure BDA0001663102500000065
Time lambdasDistribution of values as a function of gradient.
4-2 solving for (7) is:
Figure BDA0001663102500000066
solving (8) can obtain:
Figure BDA0001663102500000067
and 5, controlling the beta and theta parameters in the iteration process, and calculating and obtaining numerical values according to the formulas (6), (10) and (11) by changing the values of the beta and theta parameters to obtain the image of the iteration. Through the selection and the change of the parameters, the gradual convergence is realized in the iterative process, and finally, a satisfactory de-noising and de-blurring image is obtained.
To illustrate the effect of the method of the present invention on restoring a noisy and blurred image, a degraded image (fig. 6(a), gaussian blur, with a blur size of 9 × 9, a standard deviation of 1.5, and a noise of 0 mean and 0 variance of 0.001) is taken as an example, and the result of the method of the present invention is compared with the results of the degraded image, the RL method, and the FTVD method, as shown in fig. 6. Wherein, FIG. 6(b) is the RL restoration result, FIG. 6(c) is the FTVD method result, and FIG. 6(d) is the method result of the present invention. It can be seen from the figure that the method greatly inhibits noise while deblurring, gives consideration to noise removal and detail retention, achieves an ideal visual effect, and is superior to RL and FTVD methods. To further illustrate the superiority of the method, the above results were evaluated by objective evaluation criteria such as Structural Similarity (SSIM), peak signal-to-noise ratio (PSNR) and algorithm time, as shown in table 1 (MATLAB R2014b platform).
TABLE 1 Objective evaluation index Performance for different recovery methods
Figure BDA0001663102500000068
Figure BDA0001663102500000071

Claims (5)

1. A gradient correlation TV factor image denoising and deblurring method based on noise level is characterized by comprising the following steps:
(1) obtaining a preliminary deblurred image I by using Richardson-Lucy algorithm0；
(2) An energy equation model introduced into a traditional total variation regularization method is as follows:
Figure FDA0002846907400000011
wherein I is a potentially sharp image, I0Representing the image to be processed, k is an image blurring kernel, lambda is a constraint factor,
Figure FDA0002846907400000012
for the purpose of the first-order derivation operation,
Figure FDA0002846907400000013
for convolution operations, E (I) is the energy of image I;
(3) on the basis of the energy equation model in the step (2), introducing new variables w, v, theta, gamma and beta, and adding a gradient penalty term and a secondary gradient smoothing term to obtain the following optimization model:
Figure FDA0002846907400000014
Figure FDA0002846907400000015
wherein Ω is the image integration domain;
converting the continuous optimization model into a discrete model, wherein the solution of the discrete model relative to the image is as follows:
Figure FDA0002846907400000016
wherein, F and F-1Representing Fourier transform and inverse Fourier transform, F*RepresentsComplex conjugate operation, x, y represent two integration directions of the image,
Figure FDA0002846907400000017
is a gradient operator;
meanwhile, the discrete model is decomposed into a w subproblem and a v subproblem:
problem with the w child:
Figure FDA0002846907400000018
v sub-problem:
Figure FDA0002846907400000019
(4) gradient penalty factor distributed processing, specifically:
(4.1) introduction of gradient-dependent TV penalty factor λsThe expression is as follows:
Figure FDA00028469074000000110
wherein the content of the first and second substances,
Figure FDA0002846907400000021
variable s determines lambdasIs the overall constraint strength of s ∈ [0,2 ]]；
Figure FDA0002846907400000022
A threshold value of the gradient is indicated,
Figure FDA0002846907400000023
parameter p1、b、d、p2κ together determining λsThe respective ranges are: p 1E [0,5 ]]，b∈[50,300]，d∈[0,20]，p2∈[-10,50]，κ∈[0,0.5]；
(4.2) by using λsReplacing a constraint factor lambda, and alternately solving a w subproblem, a v subproblem and a clear image I;
(5) and controlling beta and theta parameters in the solving process, and repeatedly and iteratively solving until a clear image I is obtained.
2. The noise level-based gradient-correlation TV factor image denoising and deblurring method according to claim 1, wherein a Richardson-Lucy algorithm is combined with a total variation regularization method, and the Richardson-Lucy algorithm acquires a preliminary deblurred image as a prior result of the total variation regularization method and applies the preliminary deblurred image to a subsequent restoration process.
3. The method of claim 1, wherein a gradient-dependent TV factor image de-noising and deblurring method based on noise level is characterized by introducing a gradient-dependent TV penalty factor λsThe factors considered are: the gradient data of the noisy image is mainly distributed in a low gradient range, and a maximum value exists at the low gradient position and is distributed unevenly and sparsely to two sides.
4. The noise-level-based gradient-dependent TV factor image de-noising and deblurring method as claimed in claim 1, wherein in the step (3), the continuous optimization model is converted into a discrete model:
Figure RE-FDA0001663102490000024
。
5. a method for de-noising and deblurring gradient-dependent TV factor images based on noise level as claimed in claim 1, wherein in the step (4.2), the solution of the w sub-problem is:
Figure FDA0002846907400000025
Figure FDA0002846907400000026
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CN109559286B (en) * 2018-11-19 2022-12-06 电子科技大学 Variance gradient constraint method infrared image edge preserving denoising method
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102147915A (en) * 2011-05-06 2011-08-10 重庆大学 Method for restoring weighting sparse edge regularization image
CN103337058A (en) * 2013-07-05 2013-10-02 西北工业大学 Image blind restoration method based on blurred noise image pair joint optimization
CN106251315A (en) * 2016-08-23 2016-12-21 南京邮电大学 A kind of image de-noising method based on full variation
CN106485671A (en) * 2016-09-10 2017-03-08 天津大学 Multi-direction Weighted T V based on edge and self-similarity constraint image deblurring method
CN107133923A (en) * 2017-03-02 2017-09-05 杭州电子科技大学 A kind of blurred picture non-blind deblurring method based on self-adaption gradient sparse model
CN107369139A (en) * 2017-06-27 2017-11-21 五邑大学 A kind of noise image deblurring method being fitted based on blended data with weighting total variation

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20120011694A (en) * 2010-07-29 2012-02-08 삼성전자주식회사 Method and apparatus of processing image and medical image system employing the same
US9659351B2 (en) * 2014-03-12 2017-05-23 Purdue Research Foundation Displaying personalized imagery for improving visual acuity

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102147915A (en) * 2011-05-06 2011-08-10 重庆大学 Method for restoring weighting sparse edge regularization image
CN103337058A (en) * 2013-07-05 2013-10-02 西北工业大学 Image blind restoration method based on blurred noise image pair joint optimization
CN106251315A (en) * 2016-08-23 2016-12-21 南京邮电大学 A kind of image de-noising method based on full variation
CN106485671A (en) * 2016-09-10 2017-03-08 天津大学 Multi-direction Weighted T V based on edge and self-similarity constraint image deblurring method
CN107133923A (en) * 2017-03-02 2017-09-05 杭州电子科技大学 A kind of blurred picture non-blind deblurring method based on self-adaption gradient sparse model
CN107369139A (en) * 2017-06-27 2017-11-21 五邑大学 A kind of noise image deblurring method being fitted based on blended data with weighting total variation

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