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|Title:||Medical image reconstruction from sparse samples using simultaneous perturbation stochastic optimization||Authors:||Venkatesh, Y.V.
Medical image reconstruction
|Issue Date:||2010||Citation:||Venkatesh, Y.V., Kassim, A.A., Zonoobi, D. (2010). Medical image reconstruction from sparse samples using simultaneous perturbation stochastic optimization. Proceedings - International Conference on Image Processing, ICIP : 3369-3372. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIP.2010.5652720||Abstract:||Concerning medical images, which are known to have sparsity in either the spatial (or its derivative), DFT, DCT or curvelet domain, we propose a new approach for reconstruction from sparse samples, based on Simultaneous Perturbation Stochastic Optimization (SPSA) to minimize a nonconvex l p-norm for 0 < p < 1. The value of p chosen is such as to achieve as close an approximation to l0-norm as is computationally feasible. This approach is distinct from the homotopy-theoretic and hard-thresholding techniques of recent literature for l0- and l p-norm minimization. For lack of space, our illustrations are limited to only one each of synthetic and real images. © 2010 IEEE.||Source Title:||Proceedings - International Conference on Image Processing, ICIP||URI:||http://scholarbank.nus.edu.sg/handle/10635/83939||ISBN:||9781424479948||ISSN:||15224880||DOI:||10.1109/ICIP.2010.5652720|
|Appears in Collections:||Staff Publications|
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