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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)
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)
Estimating the angular separation between two incoherently radiating monochromatic point sources is a canonical 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 image(More)
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