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In this work we estimate Super Resolution (SR) images from a sequence of true color Compressed Sensing (CS) observations. The red, green, blue (RGB) channels are sensed separately using a measurement matrix that can be synthesized practically. The joint optimization problem to estimate the registration parameters, and the High Resolution (HR) image is transformed into a sequence of unconstrained optimization...
In this work we propose a novel framework to obtain High Resolution (HR) images from Compressed Sensing (CS) imaging systems capturing multiple Low Resolution (LR) images of the same scene. The proposed CS Super Resolution (SR) approach combines existing CS reconstruction algorithms with an LR to HR approach based on the use of a Super Gaussian (SG) regularization term. The reconstruction is formulated...
In this paper we propose a novel optimization framework to obtain High Resolution (HR) Passive Millimeter Wave (P-MMW) images from multiple Low Resolution (LR) observations captured using a simulated Compressed Sensing (CS) imaging system. The proposed CS Super Resolution (CSS-R) approach combines existing CS reconstruction algorithms with the use of Super Gaussian (SG) regularization terms on the...
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