Learning a wavelet tree for multichannel image denoising

We propose a new multichannel image denoising algorithm. To exploit important inter-channel dependencies, we first use dynamic programming to learn an explicit dyadic tree representation of the common structure of the channels. Based on this dyadic tree, optimal Haar wavelet thresholding is then applied to denoise the image. In addition to the original channels, the algorithm can employ multiple derived channels to improve tree learning. Experimental results confirm that the approach improves multichannel image denoising performance both in PSNR and in edge preservation.