Image Restoration


Our research interests in inverse problems cover recovery from:

  • highly distorted images that include blur and mixture noise originating from several distributions
  • missed samples (non-uniform or sparse sampling) in both coordinate and Fourier domains
  • quantized sparse sampling
  • optimisation techniques suitable for high dimensional data 
  • machine-learning based methods


Example results of used approach (M-estimator, quadratic regularization with analysis formulation and POCS):

Related publications:

  • Z. Hrytskiv, S. Voloshynovskiy, and A. Allen, "Radiometry imager with robust nonlinear adaptive image restoration," in Proc. IEEE Second International Conference ``The detection of abandoned land mines'', Edinburgh, UK, 1998. [pdf|bib]
  • T. Holotyak, I. Prudyus, and S. Voloshynovskiy, "Surface image formation based on sparse arrays with nonlinear signal processing," in Proc. IEEE Second International Conference ``The detection of abandoned land mines'', Edinburgh, UK, 1998. [pdf|bib]
  • I. Prudyus, S. Voloshynovskiy, and T. Holotyak, "Robust image restoration matched with adaptive aperture formation in radar imaging systems with sparse antenna arrays," in Proc. Signal Processing Conference (EUSIPCO 1998), 9th European, 1998, pp. 1-4. [pdf|bib]
  • S. Voloshynovskiy, "Robust Image Restoration Based on Concept of M-Estimation and Parametric Model of Image Spectrum," in Proc. IEEE, EURASIP 5th International Workshop on Systems, Signals and Image Processing, Zagreb, Croatia, 1998. [pdf|bib]
  • I. Prudyus, S. Voloshynovskiy, and T. Holotyak, "Adaptive aperture formation matched with radiometry image spatial spectrum," in Proc. IEEE International Microwave and Radar Conference, Krakow, Poland, 1998. [pdf|bib]
  • Z. Hrytskiv, S. Voloshynovskiy, and A. Allen, "High resolution radar imaging systems with robust image restoration," in Proc. IEEE International Microwave and Radar Conference, Krakow, Poland, 1998. [pdf|bib]
  • S. Voloshynovskiy, "Iterative image restoration with adaptive regularization and parametric constraints," Journal of Image Processing and Communications, vol. 3, iss. 3--4, pp. 73-88, 1998. [pdf|bib]
  • S. Voloshynovskiy, A. Allen, and Z. Hrytskiv, "Robust edge-preserving image restoration in the presence of non-Gaussian noise," Electronics Letters, 2000. [bib]