This paper is motivated by the challenge of high fidelity processing of images using a relatively small set of projection measurements. This is a problem of great interest in many sensing applications, for example where high photodetector counts are precluded by a combination of available power, form factor and expense. The emerging methods of dictionary learning and compressive sensing offer great potential for addressing this challenge. Combining these methods requires that the signals of interest be representable as a sparse combination of elements of some dictionary. This paper develops a method that aligns the discriminative power of such a dictionary with the physical limitations of the imaging system. Alignment is accomplished by designing a projection matrix that exposes and then aligns the modes of the noise with those of the dictionary. The design algorithm is obtained by modifying an algorithm for designing the pre-filter to maximize the rate and reliability of a Multiple Input Multiple Output (MIMO) communications channel. The difference is that in the communications problem a source is being matched to a channel, whereas in the imaging problem a channel, or equivalently the noise covariance, is being matched to a source. Our results shown that using the proposed communications design framework we can reduce reconstruction error between 20%, after only 20 projections of a 28 × 28 image, and 10% after 100 projections. Furthermore, we noticeably see the superior quality of the reconstructed images.
['William R. Carson', 'Miguel R. D. Rodrigues', 'Minhua Chen', 'Lawrence Carin', 'A. Robert Calderbank']
How to focus the discriminative power of a dictionary