NoiseLevel estimates noise level of input single noisy image.
[nlevel th num] = NoiseLevel(img,patchsize,decim,conf,itr)
nlevel: estimated noise levels.
th: threshold to extract weak texture patches at the last iteration.
num: number of extracted weak texture patches at the last iteration.
The dimension output parameters is same to channels of the input image.
img: input single image
patchsize (optional): patch size (default: 7)
decim (optional): decimation factor. If you put large number, the calculation will be accelerated. (default: 0)
conf (optional): confidence interval to determin the threshold for the weak texture. In this algorithm, this value is usually set the value very close to one. (default: 0.99)
itr (optional): number of iteration. (default: 3)
img = double(imread('img.png'));
nlevel = NoiseLevel(img);
Xinhao Liu, Masayuki Tanaka and Masatoshi Okutomi
Noise Level Estimation Using Weak Textured Patches of a Single Noisy Image
IEEE International Conference on Image Processing (ICIP), 2012.
Xinhao Liu, Masayuki Tanaka and Masatoshi Okutomi,
Single-image Noise Level Estimation for Blind Denoising,
IEEE Transactions on Image Processing, Vol.22, No.12, pp.5226-5237, 2013.
I changed line 78 in the Noise function to
tau0 = gammaincinv(conf,double(r)/2,'upper')/ 2.0 / Dtr * double(r);
tau0=gaminv(conf,double(r)/2, 2.0 * Dtr / double(r));
Now you don't need the stats toolbox
This function requires also Statistics and Machine Learning Toolbox.
Hi enji istanbouli,
I guess you have run demo.m in the tool box.
Please check the demo.m file.
True is the setting noise level. R, G, and B are noise levels for each channel.
I tried this function and thats the result that I got :
True: 5.00 R: 5.00 G: 5.06 B: 5.09
Calculation time: 2.12 [sec]
True: 10.00 R: 9.68 G: 9.75 B: 9.99
Calculation time: 0.57 [sec]
True: 20.00 R:19.56 G:19.60 B:19.67
Calculation time: 0.60 [sec]
True: 40.00 R:39.16 G:39.08 B:39.43
Calculation time: 0.61 [sec]
Can someone please explain to me what those numbers means and how to use them to evaluate noise
The output noise level is the standard deviation.
Is the output noise variance or standard deviation?
The noise level accuracy depends on the number of weak patches used. So what percentage of ALL patches would you consider to be enough for an accurate estimate?
I found the code work well for 8-bit image, however when I performed it on 16-bit image(noisy CT image), the result seemed not right, it was a complex number and very small,like 0 +1.3315e-008i. Can you tell me the reason? Thank you
Hi zeenat khan,
I have added the code for generating the mask of the extracted weak-texture region.
in your pdf you have shown images in your results but in code there are no figures. kindly let me know where should i show figure in order to see the weak texture patches. your code ony gives vallues and no figure
i dont know where toy have changed the sigma values your pdf formulas and codes are different from each other.can you kindly add comments inside your code and also let me know which paper is related to your code.icip or nle_icip
hi i need your documentation as i have to present your code
Hi Akshay Gore,
You can download the dataset from:
How to test model on Berkeley Segmentation Dataset?
I don't use the exact block toeplitz for two-dimensional image. As I commented, I used the different matrix.
my_convmtx2 generates the matrix associated to the following code:
imgh = imfilter(img,kh,'replicate');
imgh = imgh(:,2:size(imgh,2)-1,:);
Yeah I saw that code but I've tried to get it done by toeplitz matrix only for Directional Derivative operator to calculate threshold and results are coming satisfactorily but takes time longer as compared to yours code.
Once it seems to be final I'll be uploading it.
Thanks for your reply....
Hi Ashish Meshram,
You can find the source code of my_convmtx2 in the file of NoiseLevel.m.
Please check it out!
Hi Ashish Meshram,
In the matlab code, I did not use the toeplitz matrix to calculate the
derivatives. I simply use the imfilter function instead of the matrix.
The matrix associated to the derivative operation in the matlab code is
not square matrix. The reason is to handle the borders of each patch
AS mentioned in your paper at section III, equation no. 7, where by Dh and Dv are toeplitz matrix of N^2XN^2 but that not the case. For default patch size of 7 it should return 49X49 its return 35X49.
What the reason for this...
Hello Masayuki Tanaka
Could you please mention some details about the function
T = my_convmtx2(H, m, n)
And if not possible here in, please refer me to some pointer
Hi Samuele Fiorini,
Thank you for your comment.
What kind of image and how much noise did you try?
It fails in case of very low noise, I cannot figure out why. Any help?
Added the functionality of generating the mask for the weak-texture.
Add new reference to description.
Debugged threshold calculation and change default parameter.
Debugged for zero-iteration case.
Added the web information.