CN101944230A - Multi-scale-based natural image non-local mean noise reduction method - Google Patents
Multi-scale-based natural image non-local mean noise reduction method Download PDFInfo
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Abstract
The invention discloses a multi-scale-based natural image non-local mean noise reduction method, which mainly solves the problem that image textures and details are difficult to maintain in the conventional natural image noise reduction results. The method comprises the following steps of: (1) performing wavelet transform on input noise-containing natural images to decompose the noise-containing natural images into low-frequency images and high-frequency images; (2) correcting coefficients of the high-frequency images in different scales by using a BayesShrink method to obtain corrected high-frequency images; (3) reconstructing the low-frequency images and the corrected high-frequency images to obtain new low-frequency images; (4) correcting the new reconstructed low-frequency images by using a non-local mean method to obtain corrected low-frequency images; and (5) reconstructing the corrected low-frequency images and corrected high-frequency images to obtain noise-reduced images. Compared with other classic noise reduction methods, the method can better inhibit noise, also maintains the edges and texture details of the natural images, and can be used for noise reduction processing of the natural images.
Description
Technical field
The invention belongs to technical field of image processing, specifically a kind of denoising method can be used for the denoising to natural image.
Background technology
Universal day by day along with computing machine and digital image-forming equipment, Digital Image Processing more and more is subject to people's attention.Yet because the restriction of imaging device and image-forming condition, digital picture is being gathered, conversion, and the pollution that unavoidably is subjected to noise in the transportation, so image denoising is subjected to paying attention to widely as one of basic fundamental of image processing field.The noise of many reality can be similar to thinks white Gaussian noise, and the white Gaussian noise of removing in the image becomes important direction in the image denoising field.
Traditional denoising method roughly can be divided into two classes, and a class is based on the method in spatial domain, and a class is based on the method for transform domain.Compare classic methods in the denoising method of spatial domain and comprise gaussian filtering, medium filtering, bilateral filtering etc.Their common feature is exactly to utilize the continuity of local window interior pixel gray-scale value to come current pixel is carried out the gray scale adjustment.These methods have mostly been blured image when removing noise detailed information, edge of image for example, texture etc.
Because the contained information of natural image, particularly texture image has certain redundancy, people such as Buades have proposed a kind of denoising method of non-local mean.This method is that the certain window of size is got at the center with the current pixel, seeks the window that has analog structure with it in entire image, is that weights are adjusted the gray-scale value of current pixel with the similarity between the window.Because this method is in the good performance in denoising field, caused numerous scholars' extensive concern rapidly since proposing, but still there is following problem in it: 1: algorithm complexity is bigger; 2: it is not good enough that weights calculate accuracy; 3: still there be to a certain degree fuzzy in edge of image and details.
Based on the denoising method comparative maturity of multi-scale geometric analysis is exactly the various denoising methods of wavelet field, the key issue of small echo denoising is to the image wavelet coefficient Research of Statistical Model, this class basic idea is the prior probability model of statistical model as wavelet coefficient, utilize this prior imformation then, the wavelet coefficient to original image under Bayesian frame is estimated.Yet wavelet transformation still has following deficiency: 1: the sparse property of coefficient is relatively poor during higher-dimension; 2: the picture breakdown rear is limited to information, in order to overcome the deficiency of wavelet transformation, has occurred Ridgelet in recent years, Curvelet, Contourlet, Brushlet, the new tool that a series of images such as Bandelet decompose.But because the method for multi-scale geometric analysis is just done the adjustment of atrophy threshold value to the wavelet coefficient of high frequency imaging, low-frequency image is not processed,, and usually can produce Gibbs phenomenon so its final denoising effect is not very satisfactory.
Summary of the invention
The objective of the invention is to overcome the deficiency of above-mentioned prior art, proposed based on multiple dimensioned natural image non-local mean denoising method, low frequency and high-frequency information to image adopt diverse ways to handle, to realize taking into account to edge and smooth region in the natural image denoising, weaken Gibbs phenomenon greatly, improve image denoising effect.
For achieving the above object, the present invention includes following steps:
(1) the noisy natural image of input is done wavelet transformation, it is decomposed into low-frequency image and high frequency imaging;
(2) with the BayesShrink method coefficient of the high frequency imaging of different scale is revised, is obtained revised high frequency imaging:
Wherein, V
IjBe meant the high frequency imaging wavelet coefficient of j direction of i layer,
Be meant the atrophy threshold value of j direction of i layer,
Be meant the noise variance of j direction high frequency imaging of i layer, σ
xBe meant the standard deviation of high frequency imaging signal, sign is meant sign function,
(3) the revised high frequency imaging that obtains of low-frequency image that step (1) is obtained and step (2) is reconstructed, and obtains new low-frequency image;
(4) with the non-local mean method reconstruct is obtained new low-frequency image and revises, obtain revised low-frequency image:
Wherein,
Expression reconstruct obtains the similarity of new frequency image, L
1(x), L
2(y) represent that respectively reconstruct obtains in the new low-frequency image with x, y is that the size at center is the image block of n * n, and J is the set of search box pixel point, in experiment, the value of n is followed successively by 3 * 3, and 5 * 5,7 * 7, the value of J is followed successively by 7 * 7,11 * 11,21 * 21, h is level and smooth controlled variable, h=0.2 σ, V (y) are meant that reconstruct obtains the coefficient value in the new low-frequency image Search Area;
(5) revised low-frequency image that step (4) is obtained and step (2) obtain revised high frequency imaging and are reconstructed, and obtain the image after the denoising.
The present invention has the following advantages compared with prior art:
1. the present invention revises high frequency imaging with the BayesShrink method, and then farthest weakens Gibbs phenomenon owing to the non-local mean method low-frequency image is revised.
2. the present invention revises high frequency imaging with the BayesShrink method, and then can better suppress noise owing to the non-local mean method low-frequency image is revised, and keeps and recover the edge and the grain details of natural image simultaneously.
Description of drawings
Fig. 1 is a process flow diagram of the present invention;
Fig. 2 is the test pattern that the present invention uses;
Fig. 3 is the noisy image that the present invention uses;
Fig. 4 carries out figure as a result after the denoising with existing BayesShrink wavelet threshold method to Fig. 3;
Fig. 5 is the figure as a result that Fig. 3 is carried out denoising with existing two-sided filter method;
Fig. 6 is with the existing two-sided filter method is carried out denoising to Fig. 3 the figure as a result that differentiate more;
Fig. 7 is the figure as a result that Fig. 3 is carried out denoising with existing non local filtered method;
Fig. 8 is the figure as a result that Fig. 3 is carried out denoising with the inventive method.
Embodiment
With reference to accompanying drawing 1, the present invention is based on multiple dimensioned natural image non-local mean denoising method, comprise the steps:
Step 1 is done wavelet transformation to the noisy natural image of input, and it is decomposed into low-frequency image and high frequency imaging.
Digital picture is because the restriction of imaging device and image-forming condition inevitably will be subjected to the pollution of noise, and the noise of many reality can be thought Gauss's additive white noise, and noisy natural image model is:
v=u+n
Wherein, v is the gray-scale value of noisy image, u is the gray-scale value of clean image, n is Gauss's additive white noise, the present invention selects orthogonal wavelet that image is carried out 4 layers of decomposition in experiment, and the wavelet basis of use is db8, but is not limited to this decomposition method, for example also can use stationary wavelet transfer pair image to decompose, noisy natural image is decomposed into low-frequency image and high frequency imaging two parts.
Step 2 is revised the coefficient of the high frequency imaging of different scale with the BayesShrink method, obtains revised high frequency imaging.
People such as Chang are separate and obey under the hypothesis of generalized Gaussian distribution at wavelet coefficient, utilize bayes method to obtain a kind of estimation of actual signal, and then obtain BayesShrink atrophy threshold method, and its step is as follows:
2.1) the robust median method that utilizes Donoho to propose, estimate that the noise criteria of each high frequency imaging is poor:
Wherein, V
IjBe meant the high frequency imaging wavelet coefficient of j direction of i layer, V
Ij(:) is meant and takes out V
IjIn whole wavelet coefficients of containing, abs is meant and takes absolute value that median is meant and gets intermediate value;
2.2) estimate the atrophy threshold value of each high frequency imaging:
2.3) use the BayesShrink method with the correction of high frequency imaging coefficient, obtain revised high frequency imaging:
Step 3, the revised high frequency imaging that low-frequency image that step (1) is obtained and step (2) obtain is reconstructed, and obtains new low-frequency image.
In the experiment, select orthogonal wavelet that image is reconstructed, the wavelet basis of use is db8, but is not limited to this reconstructing method, for example also can use stationary wavelet that image is reconstructed, as long as keep consistent with the picture breakdown instrument of use in the step 1.
Step 4 obtains new low-frequency image with the non-local mean method to reconstruct and revises, and obtains revised low-frequency image:
Wherein,
Expression reconstruct obtains the similarity of new frequency image, L
1(x), L
2(y) represent that respectively reconstruct obtains in the new low-frequency image with x, y is that the size at center is the image block of n * n, and J is the set of search box pixel point, in experiment, the value of n is followed successively by 3 * 3, and 5 * 5,7 * 7, the value of J is followed successively by 7 * 7, and 11 * 11,21 * 21, h is level and smooth controlled variable, h=0.2 σ, σ are meant that the contained noise criteria of the noisy natural image of input is poor, and V (y) is meant that reconstruct obtains the coefficient value in the new low-frequency image Search Area.
Step 5, revised low-frequency image that step (4) is obtained and step (2) obtain revised high frequency imaging and are reconstructed, and obtain the image after the denoising.
In the experiment, select orthogonal wavelet that image is reconstructed, the wavelet basis of use is db8, but is not limited to this reconstructing method, for example also can use stationary wavelet that image is reconstructed, as long as keep consistent with the picture breakdown instrument of use in the step 1.
Effect of the present invention can further confirm by following experiment:
One. experiment condition and content
Experiment condition: test employed input picture shown in Fig. 2 and 3, wherein, Fig. 2 (a) is test pattern lena, Fig. 2 (b) is test pattern barbara, Fig. 2 (c) is test pattern peppers, Fig. 2 (d) is test pattern house, and Fig. 3 is that Fig. 2 (a) is added the noise criteria difference is 20 noisy lena image.
Experiment content: under above-mentioned experiment condition, use existing BayesShrink wavelet threshold filtering method respectively, the two-sided filter method is differentiated two-sided filter method and non local filtered method and the inventive method more Fig. 3 is experimentized.
Two. experimental result
With BayesShrink wavelet threshold method Fig. 3 is done the denoising result that obtains under the condition of five layers of decomposition as shown in Figure 4, as can be seen from Figure 4, the noise inhibiting ability of the method is limited, and edge and details exist fuzzy.
With the denoising result of two-sided filter method as shown in Figure 5, σ wherein
d=1.8, σ
r=2 * σ, the size of searching window is 11 * 11, as can be seen from Figure 5, the noise inhibiting ability of the method is better than BayesShrink wavelet threshold method, but it exists edge and the fuzzy problem of details equally;
With many resolutions two-sided filter method Fig. 3 is done two-layer wavelet decomposition, with the BayesShrink method high frequency imaging is handled, two-sided filter is handled low-frequency image, the denoising result that obtains as shown in Figure 6, σ wherein
d=1.8, σ
r=1.0 * σ, the size of searching window is 11 * 11, as can be seen from Figure 6, the method noise inhibiting ability is better than top two kinds of methods, but has edge and the not good enough problem of details conservation degree;
With the denoising result of non local filtered method as shown in Figure 7, wherein searching the window size is 21 * 21, similar window size is 7 * 7, smoothing parameter h=15 σ, σ is that the contained noise criteria of image is poor, as can be seen from Figure 7: the method noise inhibiting ability is relatively good, but can not well keep edge of image and texture information.
With the denoising result of the inventive method as shown in Figure 8, as can be seen from Figure 8: all methods of being mentioned above its denoising effect is better than, homogeneous region is also more level and smooth, and the brightness of image keeps effect better, and edge of image, details has also obtained good maintenance;
It is 10 that test pattern among Fig. 2 is added the noise criteria difference respectively, Gauss's additive white noise of 20,30 is with the evaluation index of PSNR as denoising effect, above-mentioned four kinds of existing denoising methods and method of the present invention are compared, and the denoising effect PSNR value of the whole bag of tricks is listed in the table 1.
The various denoising result contrasts of table 1
Result in the table 1 is the result after average 10 times, as can be seen from Table 1, the denoising effect of the inventive method is than BayesShrink wavelet threshold, two-sided filter, two-sided filter and the non-local mean algorithms of differentiating all improve a lot on the PSNR value more, particularly also have denoising effect preferably for the relatively abundanter barbara image of texture information.
Above experimental result shows that the present invention is better than existing other denoising method on overall performance, can eliminate Gibbs phenomenon, keeps details such as the edge of natural image and texture better smooth noise the time.
Claims (2)
1. one kind based on multiple dimensioned natural image non-local mean denoising method, comprises the steps:
(1) the noisy natural image of input is done wavelet transformation, it is decomposed into low-frequency image and high frequency imaging;
(2) with the BayesShrink method coefficient of the high frequency imaging of different scale is revised, is obtained revised high frequency imaging:
Wherein, V
IjBe meant the high frequency imaging wavelet coefficient of j direction of i layer,
Be meant the atrophy threshold value of j direction of i layer,
Be meant the noise variance of j direction high frequency imaging of i layer, σ
xBe meant the standard deviation of high frequency imaging signal, sign is meant sign function,
(3) the revised high frequency imaging that obtains of low-frequency image that step (1) is obtained and step (2) is reconstructed, and obtains new low-frequency image;
(4) with the non-local mean method reconstruct is obtained new low-frequency image and revises, obtain revised low-frequency image:
Wherein,
Expression reconstruct obtains the similarity of new frequency image, L
1(x), L
2(y) represent that respectively reconstruct obtains in the new low-frequency image with x, y is that the size at center is the image block of n * n, and J is the set of search box pixel point, in experiment, the value of n is followed successively by 3 * 3, and 5 * 5,7 * 7, the value of J is followed successively by 7 * 7,11 * 11,21 * 21, h is level and smooth controlled variable, h=0.2 σ, V (y) are meant that reconstruct obtains the coefficient value in the new low-frequency image Search Area;
(5) revised low-frequency image that step (4) is obtained and step (2) obtain revised high frequency imaging and are reconstructed, and obtain the image after the denoising.
2. the non local denoising method based on multiple dimensioned natural image according to claim 1 is characterized in that high frequency imaging is revised coefficient with the BayesShrink method described in the step (2), carries out as follows:
2a) the robust median method that utilizes Donoho to propose, estimate that the noise criteria of each high frequency imaging is poor:
Wherein, V
IjBe meant the high frequency imaging wavelet coefficient of j direction of i layer, V
Ij(:) is meant and takes out V
IjIn whole wavelet coefficients of containing, abs is meant and takes absolute value that median is meant and gets intermediate value;
2b) estimate the atrophy threshold value of each high frequency imaging:
Wherein,
It is meant the not noisy high frequency imaging standard deviation of j direction of i layer,
Be meant noisy high frequency imaging variance, n is meant the number of the high frequency imaging wavelet coefficient of j direction of i layer;
2c) use the BayesShrink method, obtain revised high frequency imaging the correction of high frequency imaging coefficient:
Symbol is identical with above-mentioned explanation in the formula.
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