Name
Abstract | ||
|---|---|---|
Wavelet shrinkage denoising has been investigated for a long time due to its simplicity and good results. SLT denoising proposed by Yacov Hel-Or et al. recently generates mapping functions (MFs), also known as shrinkage function, which are learned directly from example images using least-squares fitting. In this paper, we design MFs with the prior information properly incorporated in SLT denoising. Since coefficients in the same wavelet subband have different statistic characteristics, we first classify wavelet coefficients into different classes. Then MFs for different regions are deduced with corresponding prior model. Experimental results give a direct show that the proposed method obtains higher PSNR (Peak Signal to Noise Ratio), and improve visual quality of the denoised images. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/SITIS.2008.29 | SITIS |
Keywords | Field | DocType |
different region,wavelet transforms,statistical analysis,learning (artificial intelligence),least-square fitting method,wavelet coefficient,different class,corresponding prior model,wavelet shrinkage denoising,statistical learning theory,wavelet subband,image denoising,wavelet shrinkage denoising method,least squares approximations,different statistic characteristic,prior information,modified slt denoising method,mapping function,slt denoising,peak signal to noise ratio,least square,mathematical model,learning artificial intelligence,noise,noise measurement,noise reduction | Statistical learning theory,Noise reduction,Peak signal-to-noise ratio,Noise measurement,Statistic,Pattern recognition,Computer science,Artificial intelligence,Wavelet transform,Shrinkage function,Wavelet | Conference |
ISBN | Citations | PageRank |
978-0-7695-3493-0 | 0 | 0.34 |
References | Authors | |
3 | 2 | |