Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization

30 Apr 2014Jian ZhangChen ZhaoDebin ZhaoWen Gao

From many fewer acquired measurements than suggested by the Nyquist sampling theory, compressive sensing (CS) theory demonstrates that, a signal can be reconstructed with high probability when it exhibits sparsity in some domain. Most of the conventional CS recovery approaches, however, exploited a set of fixed bases (e.g. DCT, wavelet and gradient domain) for the entirety of a signal, which are irrespective of the non-stationarity of natural signals and cannot achieve high enough degree of sparsity, thus resulting in poor CS recovery performance... (read more)

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