This is a self-contained package including the 2-D DWT and inverse DWT routines. However, the Idwt2.m and Iidwt2.m codes are slow and inefficient. We did not try to optimize the codes at all. If you need faster routines, you should download and install the Rice Wavelet Toolbox, which is available via Rice DSP web page http://www.dsp.rice.edu INSTALLATION: download whmt.tar tar -xf whmt.tar decomposes the tar file into many matlab .m files. Main routines: hdenoise.m : denoise a 2-D image using HMT with training. You can also provide a pre-specified HMT model (no training). hmttrain.m : train HMT model for give 2-D wavelet transform. All wavelet coefficients at each scale within each subband are tied together to avoid overfitting of the model. hmtdeno.m : given 2-D wavelet transform and an HMT model, compute the posterior state probabilities. Then, use filter noisy wavelet coefficients to remove noise. hmtmodel.m : obtain an HMT model for real-world images without training. This model produces good denoising performance for many real-world images. Routines used by main routines: emhht.m : one step of EM algorithm for HH subband. Used by hmttrain.m emlht.m : same as emhht.m for LH subband. emhlt.m : same as emhht.m for HL subband. posthh.m : compute posterior state probabilities for HH subband. Used by hmtdeno.m postlh.m : same as posthh.m for LH subband posthl.m : same as posthh.m for HL subband 2-D DWT and inverse DWT routines: Idwt2.m : 2-D DWT Iidwt2.m : 2-D inverse DWT daubcqf.m : computes Daubechies wavelet filters makeh1.m, up.m : routines used by Idwt2.m and Iidwt2.m Other routines: gauss.m : gaussian density function testcon.m : compute convergence errors to stop HMT training vec2mat.m : used by hmtmodel.m Examples: example1.m : denoising example 1. generate a noisy Lena image. Train HMT model and denoise. example2.m : denoising example 2. generate a noisy Lena image. Use the HMT model in lenamodel.hmt (no training) to denoise it. example3.m : denoising example 3. generate a noisy Lena image. Use hmtmodel.m to obtain an HMT model for general real-world images. Then, use HMT denoising. Other files: lena.mat : BW Lena image normalized to [0,1] gray level used in example1.m and example2.m lenamodel.mat : HMT model (ES,PS,MU,SI matrices) for Lena with \sigma=0.1 Used in example2.m

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