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. ...