Optimal Dictionaries for Sparse Solutions of Multi-frame Blind Deconvolution

Abstract

Sparse representation of data has grown rapidly in signal processing. The benefits of sparse regularization are economy of representation of many different varieties of data, as well as control of difficult aspects of inverse problems, e.g., regularization of ill-conditioned inverse problems. Herein we represent atmospheric turbulence point-spread-functions by training optimal overcomplete dictionaries from atmospheric turbulence data. Implications for blind- deconvolution of turbulent images are discussed. The application of sparse dictionaries is demonstrated by the employment of sparse PSF representations to formulate a multi-frame blind deconvolution (MFBD) algorithm. We present results of the gain in MFBD image reconstruction by simulations of turbulent atmospheric images and the reconstruction of the corresponding images with the sparse PSF MFBD algorithm.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2014
Accession Number
ADA616750

Entities

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Atmospheric Motion
  • Computational Fluid Dynamics
  • Computational Science
  • Data Compression
  • Dictionaries
  • Differential Equations
  • Diffraction
  • Mathematical Models
  • Power Spectra
  • Refraction
  • Simulations
  • Training
  • Turbulence
  • Wavelet Transforms

Fields of Study

  • Engineering

Readers

  • Image Processing and Computer Vision.
  • Linear Algebra