Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Feature extraction using wavelet and fractal
Pattern Recognition Letters
Subspace pursuit for compressive sensing signal reconstruction
IEEE Transactions on Information Theory
Robust estimation of parameter for fractal inverse problem
Computers & Mathematics with Applications
Analysis of orthogonal matching pursuit using the restricted isometry property
IEEE Transactions on Information Theory
Fractal image coding as projections onto convex sets
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
IEEE Transactions on Signal Processing - Part II
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
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Basis pursuit (BP) and matching pursuit (MP) are two important basic recovery methods in compressive sensing (CS) research. BP can compute the global optimal solution in CS recovery problem, but its computational complexity is high and dimensional universality (regardless of 1D or 2D or higher dimensions) is not good. On the other side, the computational cost of MP is lower than BP, but the sparsity of signal needs to be known beforehand and its solution may not necessarily be global optimal. In this paper, a new CS recovery method is proposed, termed fractal pursuit (FP) which integrates the advantage of BP and MP. It acquires the prior knowledge of signal by fractal recognition to cut down the computational cost of pursuit operation, and uses fractal minimization in place of l"1-norm minimization for improving the recovery quality and dimensional universality in CS framework. Two experiments show the feasibility and performance of FP in CS recovery.