Frequency domain blind deconvolution in multiframe imaging using anisotropic spatially-adaptive denoising

In this paper we present a novel method for multiframe blind deblurring of noisy images. It is based on minimization of the energy criterion produced in the frequency domain using a recursive gradient-projection algorithm. For filtering and regularization we use the local polynomial approximation (LPA) of both the image and blur operators, and paradigm of the intersection of confidence intervals (ICI) applied for selection adaptively varying scales (window sizes) of LPA. The LPA-ICI algorithm is nonlinear and spatially-adaptive with respect to the smoothness and irregularities of the image and blur operators. Simulation experiments demonstrate efficiency and good performance of the proposed deconvolution technique.