Stochastic sampling in computer graphics
ACM Transactions on Graphics (TOG)
Digital Image Processing
ACM SIGGRAPH 2005 Papers
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Compressive image super-resolution
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
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Recently, there has been growing interest in using compressed sensing to perform imaging. Most of these algorithms capture the image of a scene by taking projections of the imaged scene with a large set of different random patterns. Unfortunately, these methods require thousands of serial measurements in order to reconstruct a high quality image, which makes them impractical for most real-world imaging applications. In this work, we explore the idea of performing sparse image capture from a single image taken in one moment of time. Our framework measures a subset of the pixels in the photograph and uses compressed sensing algorithms to reconstruct the entire image from this data. The benefit of our approach is that we can get a high-quality image while reducing the bandwidth of the imaging device because we only read a fraction of the pixels, not the entire array. Our approach can also be used to accurately fill in the missing pixel information for sensor arrays with defective pixels. We demonstrate better reconstructions of test images using our approach than with traditional reconstruction methods.