@conference {12511,
	title = {Enhancing sparsity using gradients for compressive sensing},
	booktitle = {Image Processing (ICIP), 2009 16th IEEE International Conference on},
	year = {2009},
	month = {2009/11//},
	pages = {3033 - 3036},
	abstract = {In this paper, we propose a reconstruction method that recovers images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. A key improvement of this technique is that it makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. Experiments provided also demonstrate that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors.},
	keywords = {analysis;gradient, domain;compressive, domain;image, Fourier, generalized, measurement, methods;, methods;image, Poisson, reconstruction;image, reconstruction;partial, representation;Fourier, representation;sampling, samples;robust, scenarios;sparse, sensing;enhancing, solver;sampling, sparsity;gradient},
	doi = {10.1109/ICIP.2009.5414411},
	author = {Patel, Vishal M. and Easley,G. R and Chellapa, Rama and Healy,D. M}
}
