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Ground Truth 25% measurements 4% measurements 1% measurements Figure 1: Given the block-wise compressively sensed (CS) measurements, our non-iterative algorithm is capable of high quality reconstructions. Notice how fine structures like tiger stripes or letter 'A' are recovered from only 4% measurements. Despite the expected degradation at measurement rate(More)
We present a compressive imager demonstrator based on a scalable, parallel architecture. It primarily utilizes information-optimal projections and a Piece-wise Linear Minimum Mean Square Error Estimator (PLE-MMSE) combined with a block-based statistical model of natural images. Such system delivers high-resolution images from low resolution sensor with near(More)
The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs an intermediate reconstruction. We call(More)
—Estimating the angular separation between two incoherently radiating monochromatic point sources is a canon-ical toy problem to quantify spatial resolution in imaging. In recent work, Tsang et al. showed, using a Fisher Information analysis, that Rayleigh's resolution limit is just an artifact of the conventional wisdom of intensity measurement in the(More)
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