CN103903228A - Non-local image denoising method based on HWD conversion - Google Patents

Abstract

The invention discloses a non-local image denoising method based on HWD conversion. The non-local image denoising method comprises the steps of (1) building a similar set for each reference block of an input image with noise; (2) judging singularity of the similar sets, and conducting corresponding conversion on the similar sets according to the singularity to obtain estimated values of image blocks in the similar sets; (3) according to the singularity of the similar sets, integrating the estimated values of the image blocks to obtain a basic estimation image; (4) building similar sets of the basic estimation image, building similar sets of the image with the noise according to coordinate information of the similar blocks, and conducting three-dimensional conversion on all the similar sets; (5) calculating a Wiener contraction matrix, and conducting Wiener filtering on the similar sets of the image with the noise to obtain the estimation values of the image blocks in the similar sets; (6) conducting weighted average on the estimation values of the image blocks to obtain the final denoising image. According to the non-local image denoising method based on HWD conversion, the noise can be smoothed, the edge and texture details of a natural image are better kept, and the non-local image denoising method based on HWD conversion can be used for denoising processing of the natural image.

Description

A kind of non local image de-noising method based on HWD conversion

Technical field

The invention belongs to technical field of image processing, specifically a kind of non local image de-noising method based on HWD conversion, can be used for the denoising to natural image.

Background technology

Image is the important sources of people's obtaining information, but image usually can be subject to the interference of various noises in generation and transmitting procedure.This not only has influence on the visual effect of image, has also hindered follow-up work, such as the carrying out of the work such as feature extraction, target identification.Therefore, image denoising is the vital part of image processing field.

The object of image denoising is exactly the inherent feature information that retains image in removing noise, effectively improves picture quality.At present, the method for a large amount of denoisings is suggested, and is roughly divided into spatial domain and frequency domain two classes.Spatial domain denoising method is the two-dimensional space territory at image place, to the gray-scale value processing of pixel in image, reaches the object of eliminating noise, and main classic algorithm has: mean filter, medium filtering, Wiener filtering etc.Frequency domain denoising method, by image being carried out to certain conversion, is carried out filtering processing to conversion coefficient, then is changed to spatial domain by transform domain inversion, reaches the object of denoising, and typical algorithm is wavelet threshold denoising method.Small echo has the well non-linear characteristic of approaching to one-dimensional signal, but due to its limited directivity, its excellent specific property can not be generalized to two dimension or higher dimensional space more simply, can not approach the function of line, face singularity " optimum ".In order to overcome the limitation of small echo, in recent years, multiple dimensioned geometric transformation (MGA) method was arisen at the historic moment, as Ridgelet, Contourlet, HWD etc., differentiate because these class methods have more, multiple dimensioned and multidirectional, therefore the information in presentation video better.

2007, the people such as Dabov proposed BM3D algorithm, and the method is the two dimensional image piece of the structural similarity formation three-dimensional array that flocks together, and by these three-dimensional array associating filtering, the estimated value of polymerization image block, reaches significant denoising effect.The method associating filtering part comprises three steps: similar group of three-dimension varying, contracted transformation coefficient and inverse transformation, here three-dimension varying is made up of local two-dimensional wavelet transformation and interblock one-dimensional wavelet transform, due to the limitation of two-dimensional wavelet transformation, the denoising effect in the unsmooth regions such as edge, curve is still undesirable.

Summary of the invention

The object of the invention is to overcome the deficiency of above-mentioned prior art, proposed a kind of non local image de-noising method based on HWD conversion, to realize the taking into account of edge and smooth region in natural image denoising, improve image denoising effect.

For achieving the above object, the present invention proposes a kind of non local image de-noising method based on HWD conversion, comprises the steps:

(1) using the image block in the noisy image of input as with reference to piece, for this reference block, according to Euclidean distance formula, calculate the distance of all image blocks in this reference block and its neighborhood, get distance and be less than threshold value

image block form similar group of this reference block

$S_{\mathrm{xR}}^{\mathrm{ht}}=\{Z_x^{\mathrm{ht}}:\frac{{\left|\right|Z_{\mathrm{xR}}^{\mathrm{ht}}-Z_x^{\mathrm{ht}}\left|\right|}_2^2}{N_1^{\mathrm{ht}}\&times;N_1^{\mathrm{ht}}}<{\mathrm{\&tau;}}_{\mathrm{match}}^{\mathrm{ht}},x\&Element;X\}$

Wherein, X is noisy image, and x is the pixel in X,

for reference block,

be with

centered by neighborhood in image block,

for the size of image block,

to judge two threshold values whether image block is similar;

(2) image block in similar group is carried out to two-dimensional wavelet transformation, and the large coefficient in wavelet coefficient and little coefficient are labeled as respectively to 1 and 0, according to 0 and 1 of each subband on the thickest yardstick of all image blocks in similar group distribution, judge the singularity of similar group;

(3) according to the singularity of similar group, simple similar group of singularity is carried out to two-dimensional wavelet transformation and interblock one-dimensional wavelet transform, make TIHWD conversion and interblock one-dimensional wavelet transform for similar group of singularity complexity, hard-threshold contracted transformation coefficient also carries out corresponding inverse transformation, obtains the estimated value of image block in similar group

${\hat{Y}}_x^{\mathrm{ht}}=T_{3D}^{{\mathrm{ht}}^{-1}}\left(\mathrm{\&gamma;}\left(T_{3D}^{\mathrm{ht}}\left(Z_x^{\mathrm{ht}}\right)\right)\right),Z_x^{\mathrm{ht}}\&Element;S_{\mathrm{xR}}^{\mathrm{ht}}$

Wherein,

represent the three-dimension varying being formed by two-dimensional transform and interblock one-dimensional wavelet transform,

for inverse transformation, Υ represents hard-threshold shrinkage operation, and general value is λ σ, and λ is the artificial constant of setting, and σ is that noise criteria is poor;

(4) according to the singularity of similar group, the estimated value of integral image piece, obtains basic estimated image

${\hat{Y}}^{\mathrm{basic}}\left(x\right)=\frac{\underset{x_R\&Element;X}{\mathrm{\&Sigma;}}\underset{x_m\&Element;S_{\mathrm{xR}}^{\mathrm{ht}}}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_{x_R}^{\mathrm{ht}}{\hat{Y}}_{x_m}^{\mathrm{ht}}\left(x\right)}{\underset{x_R\&Element;X}{\mathrm{\&Sigma;}}\underset{x_m\&Element;S_{\mathrm{xR}}^{\mathrm{ht}}}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_{x_R}^{\mathrm{ht}}{\mathrm{\&chi;}}_{x_m}\left(x\right)},\&ForAll;x\&Element;X$

Wherein,

it is similar group

corresponding weights,

with pixel x _mfor the image block on left summit

fundamental function, pixel

time, value is 1, otherwise is 0;

Wherein,

it is similar group

hard-threshold is shunk the number of rear nonzero coefficient;

(5) at basic estimated image

upper, build its similar group by block matching method

similar group of manual record in the coordinate information of similar:

$S_{\mathrm{xR}}^1=\{{\hat{Y}}_x^{\mathrm{basic}}:\frac{{\left|\right|{\hat{Y}}_{\mathrm{xR}}^{\mathrm{basic}}-{\hat{Y}}_x^{\mathrm{basic}}\left|\right|}_2^2}{N_1^{\mathrm{wie}}\&times;N_1^{\mathrm{wie}}}<{\mathrm{\&tau;}}_{\mathrm{match}}^{\mathrm{wie}},x\&Element;X\}$

Wherein,

with it is basic estimated image

in reference block and candidate blocks,

the size of image block,

to judge two threshold values whether image block is similar;

(6) according to similar group

in the coordinate information of similar, from noisy image, extract the image block of answering in contrast, obtain similar group of noisy image

to basic estimated image

similar group with similar group of noisy image

all carry out three-dimension varying, according to

conversion coefficient calculate Wei Na shrink matrix W, will

conversion coefficient and after W pointwise multiplies each other, then carry out inverse transformation, obtain similar group

the estimated value of middle image block

${\hat{Y}}_x^{\mathrm{wie}}=T_{3D}^{{\mathrm{wie}}^{-1}}\left(W{T}_{3D}^{\mathrm{wie}}\left(S_{\mathrm{xR}}^2\right)\right)$

$W=\frac{{\left|T_{3D}^{\mathrm{wie}}\left(S_{\mathrm{xR}}^1\right)\right|}^2}{{\left|T_{3D}^{\mathrm{wie}}\left(S_{\mathrm{xR}}^1\right)\right|}^2+{\mathrm{\&sigma;}}^2}$

Wherein,

represent the three-dimension varying being formed by two-dimension discrete cosine transform DCT and interblock one-dimensional wavelet transform,

represent inverse transformation, W is that Wei Na shrinks matrix;

(7) estimated value to the image block obtaining

weighted mean, obtains final denoising image

${\hat{Y}}^{\mathrm{final}}\left(x\right)=\frac{\underset{x_R\&Element;X}{\mathrm{\&Sigma;}}\underset{x_m\&Element;S_{\mathrm{xR}}^2}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_{x_R}^{\mathrm{wie}}{\hat{Y}}_{x_m}^{\mathrm{wie}}\left(x\right)}{\underset{x_R\&Element;X}{\mathrm{\&Sigma;}}\underset{x_m\&Element;S_{\mathrm{xR}}^2}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_{x_R}^{\mathrm{wie}}{\mathrm{\&chi;}}_{x_m}\left(x\right)},\&ForAll;x\&Element;X$

Wherein,

it is similar group

corresponding weights,

with pixel x _mfor the image block on left summit fundamental function, pixel

time, value is 1, otherwise is 0;

${\mathrm{\&omega;}}_{x_R}^{\mathrm{wie}}=\frac{1}{{\mathrm{\&sigma;}}^2{\left|\right|W\left|\right|}_2^2}$

Wherein, W is similar group

corresponding Wei Na shrinks matrix.

In above-mentioned steps (2), judge the singularity of similar group, obtain in the following manner:

(1) in similar group similar carry out three layers of wavelet decomposition, by the large coefficient mark in the wavelet coefficient after conversion

Be 1, the little coefficient of large coefficient in the wavelet coefficient after conversion be labeled as to 0:

Wherein, ω is wavelet coefficient,

be the mark value of wavelet coefficient, thresh is threshold value, and value is

(2) distribute according to 0 and 1 of each subband on all similar yardsticks the thickest in similar group, judge the singularity of similar group:

If all image blocks meet in similar group: the low frequency on the thickest yardstick, the mark value of vertical and horizontal high-frequency sub-band coefficient are 1, this similar group is judged as similar group of singularity complexity; If do not meet above-mentioned requirements, this similar group is judged as simple similar group of singularity.

The present invention has the following advantages compared with prior art:

1. the present invention carries out mark by the wavelet coefficient to similar, can judge exactly the singularity of similar group, and then different similar group selections is applicable to self conversion to singularity.

2. the present invention is according to the singularity of similar group, and to the simple similar group selection wavelet transformation of singularity, the similar group selection TIHWD conversion of singularity complexity, combines two kinds of conversion advantage separately, when denoising, and keep the edge information, grain details better.

The present invention due to similar group selection adapt to the conversion of its singularity features, can obtain basic estimated image more accurately, and then improve and in subordinate phase, build the accuracy that similar group and Wei Na shrink matrix, thereby improve the final denoising effect of image.

Accompanying drawing explanation

Fig. 1 is process flow diagram of the present invention;

Fig. 2 is the test pattern that the present invention tests use;

Fig. 3 is three layers of wavelet decomposition figure of image block of 8 × 8;

Fig. 4 is σ=25 o'clock, denoising result by existing three kinds of denoising methods to Lena image, wherein (a) figure is original Lena figure, (b) be noisy Lena figure, (c) be TIHWD bivariate threshold method denoising result, (d) being the denoising result of BM3D method, is (e) denoising result of the present invention;

Fig. 5 is σ=50 o'clock, denoising result by existing three kinds of denoising methods to house image, wherein (a) figure is original house figure, (b) be noisy house figure, (c) be TIHWD bivariate threshold method denoising result, (d) being the denoising result of BM3D method, is (e) denoising result of the present invention;

Fig. 6 is σ=35 o'clock, denoising result by existing three kinds of denoising methods to Baboon image, wherein (a) figure is original Baboon figure, (b) be noisy Baboon figure, (c) be TIHWD bivariate threshold method denoising result, (d) being the denoising result of BM3D method, is (e) denoising result of the present invention;

Fig. 7 is σ=50 o'clock, denoising result by existing three kinds of denoising methods to fingerprint image, wherein (a) figure is original fingerprint figure, (b) be noisy fingerprint figure, (c) be TIHWD bivariate threshold method denoising result, (d) being the denoising result of BM3D method, is (e) denoising result of the present invention;

Fig. 8 is partial enlarged drawing corresponding to denoising result figure in Fig. 5;

Fig. 9 is the corresponding partial enlarged drawing of denoising result figure in Fig. 7.

Embodiment

HWD(HybridWaveletsanddirectionalfilterbanks) be a kind of rarefaction representation method of break-even characteristics of image, it can extract profile and the detailed information of image effectively.

With reference to accompanying drawing 1, the invention provides a kind of non local image de-noising method based on HWD conversion, comprise the steps:

Step 1, adds to the test pattern in Fig. 2 the noise that standard deviation is σ, obtains noisy image X, builds its similar group:

1.1) in noisy image X, with step-length

get reference block x _rfor

left summit, with x _rfor in

In 39 × 39 neighborhood of the heart, according to the distance of candidate blocks in Euclidean distance formula computing reference piece and this neighborhood $d(Z_{\mathrm{xR}}^{\mathrm{ht}},Z_x^{\mathrm{ht}}):$

$d(Z_{\mathrm{xR}}^{\mathrm{ht}},Z_x^{\mathrm{ht}})=\frac{{\left|\right|Z_{\mathrm{xR}}^{\mathrm{ht}}-Z_x^{\mathrm{ht}}\left|\right|}_2^2}{{\left(N_1^{\mathrm{ht}}\right)}^2}$

Wherein,

for reference block,

for candidate blocks,

the size of image block, σ≤40 o'clock, value is 8,

be 3; When σ > 40,

value is 12,

be 4;

1.2) get distance

d is less than threshold value

similar group of candidate blocks composition reference block:

$S_{\mathrm{xR}}^{\mathrm{ht}}=\{Z_x^{\mathrm{ht}}:(Z_{\mathrm{xR}}^{\mathrm{ht}},Z_x^{\mathrm{ht}})\&le;{\mathrm{\&tau;}}_{\mathrm{match}}^{\mathrm{ht}},x\&Element;X\}$

Wherein, to judge two threshold values whether image block is similar, σ≤40 o'clock,

value is 2500; When σ > 40,

value is 5000;

Step 2, carries out two-dimensional wavelet transformation to image block in similar group, according to the large coefficient in wavelet coefficient and the distribution of little coefficient, judges the singularity of similar group:

2.1) image block in similar group is carried out to three layers of bior1.5 wavelet decomposition, the large coefficient in wavelet coefficient is labeled as to 1, the little coefficient in wavelet coefficient is labeled as to 0:

Wherein, ω is wavelet coefficient,

be the wavelet coefficient after mark, thresh is threshold value, and value is

2.2) according to 0 and 1 distribution mark value of low frequency, vertical direction and horizontal direction high-frequency sub-band on the thickest yardstick of all image blocks in similar group, judge the singularity of similar group:

As Fig. 3, if all image blocks meet in similar group: the low frequency LL on the thickest yardstick ₃, vertical direction LH ₃with horizontal direction HL ₃the mark value of high-frequency sub-band coefficient is 1, and this similar group is judged as similar group of singularity complexity; If do not meet above-mentioned requirements, this similar group is judged as simple similar group of singularity.

Step 3, carry out the collaborative filtering of transform domain to similar group:

3.1) carry out the TIHWD conversion HWD of the translation invariant form (conversion) and interblock one-dimensional wavelet transform to similar group of singularity complexity, simple similar group of singularity is carried out two-dimensional wavelet transformation (or dct transform) and interblock one-dimensional wavelet transform;

3.2) hard-threshold is shunk three-dimension varying coefficient, remakes corresponding contrary three-dimension varying:

${\hat{Y}}_x^{\mathrm{ht}}=T_{3D}^{{\mathrm{ht}}^{-1}}\left(\mathrm{\&gamma;}\left(T_{3D}^{\mathrm{ht}}\left(Z_x^{\mathrm{ht}}\right)\right)\right),Z_x^{\mathrm{ht}}\&Element;S_{\mathrm{xR}}^{\mathrm{ht}}$

Wherein,

represent the three-dimension varying being made up of two-dimensional transform (small echo, TIHWD or two-dimension discrete cosine transform DCT) and interblock one-dimensional wavelet transform, Υ is hard-threshold shrinkage operation, λ _3Dσ is threshold value, similar group of singularity complexity: λ _3Dvalue is 3 (σ≤40 o'clock) and 3.1 (when σ > 40), simple similar group of singularity: λ _3Dvalue is 2.7 (when σ <40) and 2.8 (when σ > 40), and σ is that noise criteria is poor, for inverse transformation;

Step 4, according to the singularity of similar group, the estimated value of integral image piece, obtains basic estimated image:

Add up respectively each pixel, the number of the similar group of estimated value of returning that singularity is different, gets estimated value weighted mean corresponding to more person, as the estimated value of pixel, obtains basic estimated image

${\hat{Y}}^{\mathrm{basic}}\left(x\right)=\frac{\underset{x_R\&Element;X}{\mathrm{\&Sigma;}}\underset{x_m\&Element;S_{\mathrm{xR}}^{\mathrm{ht}}}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_{x_R}^{\mathrm{ht}}{\hat{Y}}_{x_m}^{\mathrm{ht}}\left(x\right)}{\underset{x_R\&Element;X}{\mathrm{\&Sigma;}}\underset{x_m\&Element;S_{\mathrm{xR}}^{\mathrm{ht}}}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_{x_R}^{\mathrm{ht}}{\mathrm{\&chi;}}_{x_m}\left(x\right)},\&ForAll;x\&Element;X$

Wherein,

it is similar group

corresponding weights, with pixel x _mfor the image block on left summit

fundamental function, pixel

time,

value is 1, otherwise is 0;

Wherein,

it is similar group

hard-threshold is shunk the number of rear nonzero coefficient;

Step 5, on basic estimated image, builds its similar group by block matching method

the coordinate information of similar in similar group of manual record:

$S_{\mathrm{xR}}^1=\{{\hat{Y}}_x^{\mathrm{basic}}:\frac{{\left|\right|{\hat{Y}}_{\mathrm{xR}}^{\mathrm{basic}}-{\hat{Y}}_x^{\mathrm{basic}}\left|\right|}_2^2}{{\left(N_1^{\mathrm{wie}}\right)}^2}<{\mathrm{\&tau;}}_{\mathrm{match}}^{\mathrm{wie}},x\&Element;X\}$

Wherein, with

be reference block and the candidate blocks in basic drawing for estimate, the size of piece is

to judge two threshold values whether image block is similar, and the step-length here

value is 6, σ≤40 o'clock,

value is 400, value is 8; When σ > 40,

value is 3500, value is 11;

Step 6, the coordinate information obtaining according to step 5 extracts corresponding image block from noisy image, obtains similar group

carry out the collaborative filtering of transform domain:

6.1) similar group to basic estimated image

carry out three-dimension varying, calculate Wei Na according to conversion coefficient and shrink matrix:

$W=\frac{{\left|T_{3D}^{\mathrm{wie}}\left(S_{\mathrm{xR}}^1\right)\right|}^2}{{\left|T_{2D}^{\mathrm{wie}}\left(S_{\mathrm{xR}}^1\right)\right|}^2+{\mathrm{\&sigma;}}^2}$

Wherein,

represent the three-dimension varying being formed by two-dimension discrete cosine transform DCT and interblock one-dimensional wavelet transform;

6.2) to similar group

carry out same three-dimension varying, will

conversion coefficient and after W pointwise multiplies each other, then inverse transformation, obtains similar group

the estimated value of middle image block:

${\hat{Y}}_x^{\mathrm{wie}}=T_{3D}^{{\mathrm{wie}}^{-1}}\left(W{T}_{3D}^{\mathrm{wie}}\left(S_{\mathrm{xR}}^2\right)\right)$

Wherein, W is that Wei Na shrinks matrix,

represent contrary three-dimension varying;

Step 7, to similar group middle image block estimated value weighted mean, obtains final denoising image

${\hat{Y}}^{\mathrm{final}}\left(x\right)=\frac{\underset{x_R\&Element;X}{\mathrm{\&Sigma;}}\underset{x_m\&Element;S_{\mathrm{xR}}^2}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_{x_R}^{\mathrm{wie}}{\hat{Y}}_{x_m}^{\mathrm{wie}}\left(x\right)}{\underset{x_R\&Element;X}{\mathrm{\&Sigma;}}\underset{x_m\&Element;S_{\mathrm{xR}}^2}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_{x_R}^{\mathrm{wie}}{\mathrm{\&chi;}}_{x_m}\left(x\right)},\&ForAll;x\&Element;X$

Wherein,

it is similar group

corresponding weights,

with pixel x _mfor the image block on left summit

fundamental function, pixel time,

value is 1, otherwise is 0;

${\mathrm{\&omega;}}_{x_R}^{\mathrm{wie}}=\frac{1}{{\mathrm{\&sigma;}}^2{\left|\right|W\left|\right|}_2^2}$

Wherein, W is similar group

corresponding Wei Na shrinks matrix.

Effect of the present invention can further confirm by following experiment:

One. experiment condition and content

Experiment condition: as shown in Figure 2, in experiment, denoising method is all to use matlab Programming with Pascal Language to realize to the test pattern that experiment is used.

Experiment content: under above-mentioned experiment condition, 1. bivariate threshold value, BM3D denoising method and the present invention test to use respectively TIHWD.Wherein, the decomposition number of plies in TIHWD bivariate threshold method is [33000], and what BM3D method was used is the parameter in its corresponding article, in the first stage of the present invention, σ≤40 o'clock, two-dimensional transform is bior1.5 wavelet transformation and TIHWD conversion, and when σ > 40, two-dimensional transform is dct transform and TIHWD conversion, the decomposition number of plies of TIHWD conversion is [2,0], one-dimensional wavelet transform is haar wavelet transformation, and the parameter of subordinate phase all arranges consistent with parameter in BM3D.

Two. experimental result

To the denoising result of the test pattern in Fig. 2 as shown in FIG. 4,5,6, 7, wherein Fig. 4 (a) is test pattern Lena, size is that 256 × 256, Fig. 4 (b) is that Fig. 4 (a) is added to standard deviation is the noisy Lena image of 25 noises; Fig. 5 (a) is test pattern house, and size is that 256 × 256, Fig. 5 (b) is that 5 (a) are added to standard deviation is the noisy house image of 50 noises; Fig. 6 (a) is test pattern Baboon, and size is that 512 × 512, Fig. 6 (b) is that 6 (a) are added to standard deviation is the noisy Baboon image of 35 noises; Fig. 7 (a) is test pattern fingerprint, and size is that 512 × 512, Fig. 7 (b) is that 7 (a) are added to standard deviation is the noisy fingerprint image of 50 noises; At Fig. 4 in Fig. 7, (c) be the denoising image that TIHWD bivariate threshold method obtains, (d) be the denoising image that BM3D method obtains, (e) be the denoising image that the present invention obtains, Fig. 8 and Fig. 9 are respectively the partial enlarged drawing of corresponding denoising result in Fig. 5 and Fig. 7.

Use TIHWD bivariate threshold method denoising result in Fig. 7, as shown in (c), can find out from result figure and partial enlarged drawing as Fig. 4, the method can keep detailed information preferably, but in image, has produced obvious Gibbs' effect;

Use BM3D method denoising result if Fig. 4 is in Fig. 7 as shown in (d), can find out from result figure and partial enlarged drawing, the method all has denoising performance preferably at homogeneous area and fringe region, compare TIHWD bivariate threshold method, in result figure, almost there is no Gibbs' effect, but in denoising, can make fuzzy and the continuity that can not keep the edge information of part edge;

The inventive method denoising result is if Fig. 4 is in Fig. 7 as shown in (e), can find out from result figure and partial enlarged drawing, than two kinds of above-mentioned denoising methods, the present invention is removing in noise, the detailed information such as keep the edge information and unsmooth region well.

Quantitative evaluation index by the Y-PSNR PSNR value of each figure in Fig. 2 as denoising result, its computing method are:

$\mathrm{PSNR}=101g\left[\frac{u_{\max }^2}{\frac{1}{\left|N\right|}\underset{i\&Element;N}{\mathrm{\&Sigma;}}{[\hat{v}\left(i\right)-u\left(i\right)]}^2}\right]$

U (i) is the original nothing figure that makes an uproar,

for the result figure after denoising, u _max=max{u (i), i ∈ N}, N presentation video size.The PSNR value of above-mentioned existing two kinds of denoising methods and denoising result of the present invention is listed in table 1.

The contrast of table 1 denoising result

From testing, the present invention is with respect to TIHWD bivariate threshold method and BM3D denoising method, and the visual effect of denoising figure and PSNR evaluation index all increase, when the present invention can smooth noise, the edge and the grain details that keep better image, have better denoising effect.

In literary composition, there is 1. coming from foreign language document: EslamiR, RadhaH., Anewfamilyofnonredundanttransformsusinghybridwaveletsand directionalfilterbanks.IEEETrans.ImageProcessing, 2007,16 (4): 1152-1167.

Claims (2)

1. the non local image de-noising method based on HWD conversion, is characterized in that: comprise the steps:

(1) using the image block in the noisy image of input as with reference to piece, for this reference block, according to Euclidean distance formula, calculate the distance of all image blocks in this reference block and its neighborhood, get distance and be less than threshold value

image block form similar group of this reference block

$S_{\mathrm{xR}}^{\mathrm{ht}}=\{Z_x^{\mathrm{ht}}:\frac{{\left|\right|Z_{\mathrm{xR}}^{\mathrm{ht}}-Z_x^{\mathrm{ht}}\left|\right|}_2^2}{N_1^{\mathrm{ht}}\&times;N_1^{\mathrm{ht}}}<{\mathrm{\&tau;}}_{\mathrm{match}}^{\mathrm{ht}},x\&Element;X\}$

Wherein, X is noisy image, and x is the pixel in X,

for reference block,

be with

centered by neighborhood in image block,

for the size of image block,

to judge two threshold values whether image block is similar;

(2) image block in similar group is carried out to two-dimensional wavelet transformation, and the large coefficient in wavelet coefficient and little coefficient are labeled as respectively to 1 and 0, according to 0 and 1 of each subband on the thickest yardstick of all image blocks in similar group distribution, judge the singularity of similar group;

(3) according to the singularity of similar group, simple similar group of singularity is carried out to two-dimensional wavelet transformation and interblock one-dimensional wavelet transform, make TIHWD conversion and interblock one-dimensional wavelet transform for similar group of singularity complexity, hard-threshold contracted transformation coefficient also carries out corresponding inverse transformation, obtains the estimated value of image block in similar group

${\hat{Y}}_x^{\mathrm{ht}}=T_{3D}^{{\mathrm{ht}}^{-1}}\left(\mathrm{\&gamma;}\left(T_{3D}^{\mathrm{ht}}\left(Z_x^{\mathrm{ht}}\right)\right)\right),Z_x^{\mathrm{ht}}\&Element;S_{\mathrm{xR}}^{\mathrm{ht}}$

Wherein,

represent the three-dimension varying being formed by two-dimensional transform and interblock one-dimensional wavelet transform,

for inverse transformation, Υ represents hard-threshold shrinkage operation, and general value is λ σ, and λ is the artificial constant of setting, and σ is that noise criteria is poor;

(4) according to the singularity of similar group, the estimated value of integral image piece, obtains basic estimated image

${\hat{Y}}^{\mathrm{basic}}\left(x\right)=\frac{\underset{x_R\&Element;X}{\mathrm{\&Sigma;}}\underset{x_m\&Element;S_{\mathrm{xR}}^{\mathrm{ht}}}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_{x_R}^{\mathrm{ht}}{\hat{Y}}_{x_m}^{\mathrm{ht}}\left(x\right)}{\underset{x_R\&Element;X}{\mathrm{\&Sigma;}}\underset{x_m\&Element;S_{\mathrm{xR}}^{\mathrm{ht}}}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_{x_R}^{\mathrm{ht}}{\mathrm{\&chi;}}_{x_m}\left(x\right)},\&ForAll;x\&Element;X$

Wherein,

it is similar group

corresponding weights,

with pixel x _mfor the image block on left summit

fundamental function, pixel

time,

value is 1, otherwise is 0;

Wherein,

it is similar group

hard-threshold is shunk the number of rear nonzero coefficient;

(5) at basic estimated image upper, build its similar group by block matching method

similar group of manual record

in the coordinate information of similar:

$S_{\mathrm{xR}}^1=\{{\hat{Y}}_x^{\mathrm{basic}}:\frac{{\left|\right|{\hat{Y}}_{\mathrm{xR}}^{\mathrm{basic}}-{\hat{Y}}_x^{\mathrm{basic}}\left|\right|}_2^2}{N_1^{\mathrm{wie}}\&times;N_1^{\mathrm{wie}}}<{\mathrm{\&tau;}}_{\mathrm{match}}^{\mathrm{wie}},x\&Element;X\}$

Wherein,

with it is basic estimated image

in reference block and candidate blocks,

the size of image block, to judge two threshold values whether image block is similar;

(6) according to similar group

in the coordinate information of similar, from noisy image, extract the image block of answering in contrast, obtain similar group of noisy image to basic estimated image

similar group

with similar group of noisy image all carry out three-dimension varying, according to

conversion coefficient calculate Wei Na shrink matrix W, will

conversion coefficient and after W pointwise multiplies each other, then carry out inverse transformation, obtain similar group

the estimated value of middle image block

${\hat{Y}}_x^{\mathrm{wie}}=T_{3D}^{{\mathrm{wie}}^{-1}}\left(W{T}_{3D}^{\mathrm{wie}}\left(S_{\mathrm{xR}}^2\right)\right)$

$W=\frac{{\left|T_{3D}^{\mathrm{wie}}\left(S_{\mathrm{xR}}^1\right)\right|}^2}{{\left|T_{3D}^{\mathrm{wie}}\left(S_{\mathrm{xR}}^1\right)\right|}^2+{\mathrm{\&sigma;}}^2}$

Wherein,

represent the three-dimension varying being formed by two-dimension discrete cosine transform DCT and interblock one-dimensional wavelet transform, represent inverse transformation, W is that Wei Na shrinks matrix;

(7) estimated value to the image block obtaining

weighted mean, obtains final denoising image

${\hat{Y}}^{\mathrm{final}}\left(x\right)=\frac{\underset{x_R\&Element;X}{\mathrm{\&Sigma;}}\underset{x_m\&Element;S_{\mathrm{xR}}^2}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_{x_R}^{\mathrm{wie}}{\hat{Y}}_{x_m}^{\mathrm{wie}}\left(x\right)}{\underset{x_R\&Element;X}{\mathrm{\&Sigma;}}\underset{x_m\&Element;S_{\mathrm{xR}}^2}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_{x_R}^{\mathrm{wie}}{\mathrm{\&chi;}}_{x_m}\left(x\right)},\&ForAll;x\&Element;X$

Wherein,

it is similar group

corresponding weights,

with pixel x _mfor the image block on left summit fundamental function, pixel

time,

value is 1, otherwise is 0;

${\mathrm{\&omega;}}_{x_R}^{\mathrm{wie}}=\frac{1}{{\mathrm{\&sigma;}}^2{\left|\right|W\left|\right|}_2^2}$

Wherein, W is similar group

corresponding Wei Na shrinks matrix.

2. a kind of non local image de-noising method based on HWD conversion according to claim 1, is characterized in that: in described step (2), judge the singularity of similar group, obtain in the following manner:

(1) in similar group similar carry out three layers of wavelet decomposition, the large coefficient in the wavelet coefficient after conversion is labeled as to 1, the little coefficient of large coefficient in the wavelet coefficient after conversion is labeled as to 0:

Wherein, ω is wavelet coefficient,

be the mark value of wavelet coefficient, thresh is threshold value, and value is

(2) distribute according to 0 and 1 of each subband on all similar yardsticks the thickest in similar group, judge the singularity of similar group:

If all image blocks meet in similar group: the low frequency on the thickest yardstick, the mark value of vertical and horizontal high-frequency sub-band coefficient are 1, this similar group is judged as similar group of singularity complexity; If do not meet above-mentioned requirements, this similar group is judged as simple similar group of singularity.

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