Compressive Blind Image Deconvolution

Compressive Blind Image Deconvolution

We propose a novel blind image deconvolution (BID) regularization framework for compressive sensing (CS) based imaging systems capturing blurred images. The proposed framework relies on a constrained optimization technique, which is solved by a sequence of unconstrained sub-problems, and allows the...

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Veröffentlicht in:IEEE transactions on image processing 2013-10, Vol.22 (10), p.3994-4006
Hauptverfasser: Amizic, Bruno, Spinoulas, Leonidas, Molina, Rafael, Katsaggelos, Aggelos K.
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Applied sciences
blind image deconvolution
compressive sensing
constrained optimization
Deconvolution
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Image coding
Image processing
Image reconstruction
Information, signal and communications theory
Inverse methods
Optimization
Reconstruction algorithms
Sampling, quantization
Signal and communications theory
Signal processing
Signal, noise
Sparse matrices
Studies
Telecommunications and information theory
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Beschreibung
Zusammenfassung:We propose a novel blind image deconvolution (BID) regularization framework for compressive sensing (CS) based imaging systems capturing blurred images. The proposed framework relies on a constrained optimization technique, which is solved by a sequence of unconstrained sub-problems, and allows the incorporation of existing CS reconstruction algorithms in compressive BID problems. As an example, a non-convex lp quasi-norm with 0
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2013.2266100