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Signal Processing Algorithms Hans G. Feichtinger (Univ. of Vienna) NuHAG. Previous Work. * Mathematical methods for image processing (interdisciplinary FSP 1994-2000) * Gabor Analysis (Book, 1998) * Algorithms for irregular sampling (e.g., geophysics).
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Signal Processing AlgorithmsHans G. Feichtinger (Univ. of Vienna) NuHAG Previous Work * Mathematical methods for image processing (interdisciplinary FSP 1994-2000) * Gabor Analysis (Book, 1998) * Algorithms for irregular sampling (e.g., geophysics) Objectives of Planned Work Establish new parallel basic algorithms for * scattered data approximation in 2D/3D * Gabor analysis for images (denoising, space variant filtering)
Signal Processing AlgorithmsHans G. Feichtinger (Univ. of Vienna) Scattered Data (irregular sampling) Problem Signal model: smooth function f (e.g., band-limited) Task: Recovery of f from sampling values f(ti) Methods: linear recovery using iterations: f(t) =Sif(ti)ei (t) Numerical aspects: fast iterative (CG-based) algorithms and well structured (e.g., Toeplitz) system matrix.
Signal Processing AlgorithmsHans G. Feichtinger (Univ. of Vienna) • image restoration (lost pixel problem) • geophysical data approximation • nearest neighborhood approximation
Signal Processing Algorithms (Scattered Data) Hans G. Feichtinger (Univ. of Vienna)
Signal Processing AlgorithmsHans G. Feichtinger (Univ. of Vienna) Background within NUHAG * variety of iterative algorithms (CG); * guaranteed rates of convergence; * established robustness (e.g., jitter error); * good locality possible (T. Werther); * adaptive weights improve condition; * no a priori information of f is required (function spaces);
Signal Processing AlgorithmsHans G. Feichtinger (Univ. of Vienna) Scattered Data or Irregular Sampling Problem (1st step): 2D-Voronoi method = nearest neighborhood interpolation Fourier-based method applied to color images
Signal Processing Algorithms (Scattered Data) Hans G. Feichtinger (Univ. of Vienna) Irregular sampling Reconstruction
Signal Processing AlgorithmsHans G. Feichtinger (Univ. of Vienna) Explicit and hidden Parallelism A) Evident opportunities * frequent FFT2 * establishing system (Toeplitz) matrix * parallel variants of POCS B) Hidden parallelism and new problems * local iteration versus data exchange * real time applications * time / space variant smoothness * time variant Gabor based filters
Signal Processing Algorithms (Scattered Data) Hans G. Feichtinger (Univ. of Vienna) A possible application: move restoration
Signal Processing Algorithms (Scattered Data) Hans G. Feichtinger (Univ. of Vienna) Reconstruction with nearest neighbourhood
Signal Processing Algorithms (Scattered Data) Hans G. Feichtinger (Univ. of Vienna) Reconstruction with adaptive filtering respecting directional information
Signal Processing AlgorithmsHans G. Feichtinger (Univ. of Vienna) Foundations of Gabor Analysis Two (mutually dual) equivalent fares both involving a STFT (for some window g): STFTgf(t,r)=[FT(Ttg*f)](r) A) Recover signal f from sampled STFT (eliminate redundancy by sampling over some TF-lattice) B) Gabor´s “Atomic Approach“: Expand a given signal as series of time-frequency shifted atoms Problem: good locality requires non-orthogonality of system Joint Solution: “dual“ Gabor-atoms (for given g and lattice).
Signal Processing AlgorithmsHans G. Feichtinger (Univ. of Vienna) Operations based on Gabor Analysis • Signal denoising (*) • time-variant filtering • texture analysis (image segmentation) • foveation (focus of attention) • musical transcription • image compression (*)
Signal Processing Algorithms (Gabor Analysis) Hans G. Feichtinger (Univ. of Vienna) The Time-Frequency-representation of a sound signal showing the temporal frequency variation freqency time
Signal Processing Algorithms (Gabor Analysis) Hans G. Feichtinger (Univ. of Vienna)
Signal Processing Algorithms (Gabor Analysis) Hans G. Feichtinger (Univ. of Vienna)
Signal Processing Algorithms (Gabor Analysis) Hans G. Feichtinger (Univ. of Vienna)