Model-Guided Adaptive Recovery of Compressive Sensing
DCC '09 Proceedings of the 2009 Data Compression Conference
Low bit-rate image compression via adaptive down-sampling and constrained least squares upconversion
IEEE Transactions on Image Processing
Decoding by linear programming
IEEE Transactions on Information Theory
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We propose a new image acquisition and recovery strategy of hybrid sensing (HS) that combines random sampling of compressive sensing (CS) and uniform down sampling. HS lets the two sampling schemes complement each other so that one can have the best of both worlds: signal-independent sparse sampling that is the hallmark of CS, and locally adaptive signal reconstruction that is afforded by uniform sampling. We suggest a few important applications of HS in image acquisition and communication, such as multispectral imaging, multiple description image coding, multiview video, and ultra-high throughput imaging (e.g., functional medical imaging).