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This paper demonstrates a computational image sensor capable of implementing compressive sensing operations. Instead of sensing raw pixel data, this image sensor projects the image onto a separable 2-D basis set and measures the corresponding expansion coefficients. The inner products are computed in the analog domain using a computational focal plane and an analog vector-matrix multiplier (VMM). This is more than mere postprocessing, as the processing circuity is integrated as part of the sensing circuity itself. We implement compressive imaging on the sensor by using pseudorandom vectors called noiselets for the measurement basis. This choice allows us to reconstruct the image from only a small percentage of the transform coefficients. This effectively compresses the image without any digital computation and reduces the throughput of the analog-to-digital converter (ADC). The reduction in throughput has the potential to reduce power consumption and increase the frame rate. The general architecture and a detailed circuit implementation of the image sensor are discussed. We also present experimental results that demonstrate the advantages of using the sensor for compressive imaging rather than more traditional coded imaging strategies.