Dearl all: Hi all, I need to use wavelet based method to remove Gaussian white noise on an image. I have some confusion and would like to ask you: 1. What wavelet basis I should choose for decomposing the image? I intend to use Haar , DB2, db3, db4 to test the SNR to see what is the best. However I am considering for Gaussian Noise, if there are some best wavelet bases for this task, if their properties (i.e support, vanishing moments and symmetriness) affect anything on the quality of the filter ( specifically the SNR). 2. I heard about the so called " best wavelet basis" which we choose from a wavelet tree which can be the best to approximate the signal. There are some algorithm to search for it. I am wondering if I need to use that "best wavelet basis" for my Gaussian Noise filter based wavelet method. What criteria I should choose to search for that best wavelet basis? 3. How deep should I decompose the image, specifically do I need to decompose it until the maximum of the possible scale j (Log2 N, I believe)? 4. Is soft thresholding is always better than hard thresholding? Thank you very much. Regards, AELOVER11
wavelet based method for removing Gaussian Noise
Started by ●April 17, 2007