Applications of multiscale hierarchical decomposition to blind deconvolution

TL;DR

This paper introduces a multiscale hierarchical decomposition method for blind deconvolution, improving detail recovery and interpretability while reducing parameter tuning, with proven convergence and competitive results.

Contribution

The paper presents a novel multiscale hierarchical approach with convergence guarantees and explicit regularizer minimizers for blind deconvolution, enhancing interpretability and reducing parameter tuning.

Findings

Method achieves comparable results to existing approaches.

Enforces positivity constraint on Fourier transform to break symmetry.

Provides meaningful interpretation of iteration steps.

Abstract

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.