If you have a multiscale likelihood-based image denoising approach, then consider to implement this toolbox with the potential to boost the performance of your proposed approach but in a very efficient way. Translation Invariant (TI) cycle spinning is an effective method for removing artifacts from images. This toolbox can calculate the TI version for general multiscale likelihood methods in O(n logn) time (assuming the original method uses O(n) time), for both Gaussian noised and Poisson noised images. It also can be used for general multiscale denoising approaches provided by the users. Please refer to the "DEMO" files for the demonstration. The corresponding paper is extremely useful to understand the mechanisms of the algorithm, which can be found at http://www4.stat.ncsu.edu/~ghosal/papers/TI_denoise.pdf. Please feel free to contact the author Meng Li (email: firstname.lastname@example.org) for any comments or suggestions.
MENG (2023). Fast Translation Invariant Multiscale Image Denoising (2D, 3D, Poisson, Gaussian images) (https://www.mathworks.com/matlabcentral/fileexchange/48514-fast-translation-invariant-multiscale-image-denoising-2d-3d-poisson-gaussian-images), MATLAB Central File Exchange. 检索来源 .
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