Applications of multiscale hierarchical decomposition to blind deconvolution
<|reference_start|>Applications of multiscale hierarchical decomposition to blind deconvolution: The blind image deconvolution is a challenging, highly ill-posed nonlinear inverse problem. We introduce a Multiscale Hierarchical Decomposition Method (MHDM) that is iteratively solving variational problems with adaptive data and regularization parameters, towards obtaining finer and finer details of the unknown kernel and image. We establish convergence of the residual in the noise-free data case, and then in the noisy data case when the algorithm is stopped early by means of a discrepancy principle. Fractional Sobolev norms are employed as regularizers for both kernel and image, with the advantage of computing the minimizers explicitly in a pointwise manner. In order to break the notorious symmetry occurring during each minimization step, we enforce a positivity constraint on the Fourier transform of the kernels. Numerical comparisons with a single-step variational method and a non-blind MHDM show that our approach produces comparable results, while less laborious parameter tuning is necessary at the price of more computations. Additionally, the scale decomposition of both reconstructed kernel and image provides a meaningful interpretation of the involved iteration steps.<|reference_end|>
@article{wolf2024applications,
    title={Applications of multiscale hierarchical decomposition to blind
  deconvolution},
    author={Tobias Wolf, Stefan Kindermann, Elena Resmerita, Luminita Vese},
    journal={arXiv preprint arXiv:2409.08734},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08734},
    primaryClass={math.NA cs.NA}
}