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DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING

Citation Author(s):
Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade
Submitted by:
Ajit Rajwade
Last updated:
19 March 2016 - 4:43am
Document Type:
Poster
Event:

Abstract

Imaging techniques involve counting of photons striking a detector.
Due to fluctuations in the counting process, the measured
photon counts are known to be corrupted by Poisson
noise. In this paper, we propose a blind dictionary learning
framework for the reconstruction of photographic image data
from Poisson corrupted measurements acquired by a compressive
camera. We exploit the inherent non-negativity of the
data by modeling the dictionary as well as the sparse dictionary
coefficients as non-negative entities, and infer these directly
from the compressed measurements in a Poisson maximum
likelihood framework. We experimentally demonstrate
the advantage of this in situ dictionary learning over commonly
used sparsifying bases such as DCT or wavelets, especially
on color images.

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