Image denoising techniques

1. An image denoising method comprising:

decomposing an image signal into a wavelet domain using a discrete wavelet transform (DWT), wherein coefficients are grouped in sub-bands of different scales and orientations, including determining wavelet domain noise model and a non-parametric multivariate wavelet-description of an image; and

determining a noise corrected image in the wavelet domain from the image signal, the wavelet-domain noise model and the non-parametric multivariate wavelet description including Gaussian filtering of probability density function applying a Bayesian multivariate minimum mean square error (MMSE) approximation using two-dimensional marginal projections of each given coefficient, the four nearest neighbors in a same sub-band of the given coefficient and a parent at the same pixel location and orientation in the next coarsest scale.

2. The method according to claim 1 , wherein the discrete wavelet transform (DWT) is a Cohen-Daubeschies-Feauveau based discrete wavelet transform.

3. The method according to claim 1 , wherein the discrete wavelet transform (DWT) is a convolution-based discrete wavelet transform.

4. The method according to claim 1 , further comprising applying the Bayesian multivariate minimum mean square (MMSE) approximation using two-dimensional marginal projections of each given coefficient and the four nearest neighbors for a coarsest scale sub-bands that have no parent.

5. The method according to claim 1 , transforming the noise corrected image in the wavelet domain back into a spatial domain using a transfer function including:

approximating with a one-dimensional estimator of the given wavelet coefficient;

adding the one-dimensional approximation of an independent contribution of neighbor coefficients; and

adding the approximation of a separable product of one-dimensional functions.

6. The method according to claim 5 , wherein the transfer function includes twelve 1-dimensional functions.

7. One or more non-transitory computing device readable media including instructions which when executed cause a computing device to implement an image denoising method comprising:

determining a wavelet domain noise model and a non-parametric multivariate wavelet-domain description of a signal probability density function for an image signal; and

determining a noise corrected image in the wavelet domain from the image signal, wavelet-domain noise model and the non-parametric multivariate wavelet-domain description of the signal probability density function applying a Bayesian multivariate minimum mean square error (MMSE) estimation using two-dimensional marginal projections of each given coefficient, the four nearest neighbors in a same sub-band of the given coefficient and a parent at the same pixel location and orientation in the next coarsest scale.

8. The image denoising method of claim 7 , further comprising Gaussian filtering the MMSE estimation.

9. The image denoising method of claim 7 , wherein apply the Bayesian multivariate minimum mean square error (MMSE) estimation includes:

calculating the variance of a given area if a spatial-domain variance, of the given area, filtered out of its lowest-pass frequencies, is approximately equal to the lowest variance found in a spatial domain of a corresponding smaller size area; and

calculating an independent identically distributed Gaussian noise model with a spatial domain variance equal to a lowest variance of the corresponding smaller size area.

10. The image denoising method of claim 7 , further comprising transforming the noise corrected image in the wavelet domain back into a spatial domain using a transfer function including:

approximating with a one-dimensional estimator of a given wavelet coefficient;

adding the one-dimensional approximation of an independent contribution of neighbor coefficients; and

adding the approximation of a separable product of one-dimensional functions.

11. One or more non-transitory computing device readable media including instructions which when executed cause a computing device to implement an image denoising system comprising:

a noise analyzer for receiving an image and generating a wavelet domain noise model and a non-parametric, multivariate wavelet-description of the image using a discrete wavelet transform (DWT);

a noise correction engine for receiving the image, the wavelet-domain noise model and the non-parametric, multivariate wavelet description and generating a noise corrected image in the wavelet domain by applying a multivariate minimum mean square error (MMSE) approximation using two-dimensional marginal projections of each given coefficient, the four nearest neighbors in a same sub-band of the given coefficient and a parent at the same pixel location and orientation in the next coarsest scale; and

transforming the noise corrected image in the wavelet domain back into the image domain using a transfer function including approximating with a one-dimensional estimator of the given wavelet coefficient, adding the one-dimensional approximation of an independent contribution of neighbor coefficients, and adding the approximation of a separable product of one-dimensional functions.

12. The image denoising system of claim 11 , wherein the noise correction engine comprises:

a cache grid effect aligning tiles of the image to a specified grid; and

a denoiser render effect for wavelet-domain filter tile aligned to the specified grid.