A Wavelet Local Denoising-Reconstruction Algorithm Based on Non-separable MRA
A wavelet reconstruction algorithm based on non-separable MRA is proposed.In the algorithm the projection data is disposed with two-channel wavelet decomposement to obtain the approximate coefficient and the detail coefficient of the image,and then after these coefficients are denoised with soft-thresholding,the image is reconstructed through reverse wavelet transform.With this algorithm,the complexity can be reduced.This algorithm not only has shorter reconstructing time,but also can denoise.
A weightedL2 norm is introduced in which Markov operators, e.g., associated with noisy maps, are contracting provided the kernel (i.e., the transitional distribution) is smooth enough. This results in strong relaxational properties of noisy maps. Similar to this norm, integral functionals appear useful when studying spatiotemporal chaos and random fields.