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- Title
Recovery of a Compressed Sensing CT Image Using a Smooth Re-weighted Function- Regularized Least-Squares Algorithm.
- Authors
Peng-bo Zhou; Kang LI; Wei Wei; Woźniak, Marcin; Zhuo-ming Du; Hong-an Li
- Abstract
It is challenging to recover the required compressed CT (Computed Tomography, CT) image, which is got by transferred through the internet or is stored in a signal library after being compressed. We present a recovery method for compressed sensing CT images. At present, minimizing 0-norm, 1-norm and p-norm is used to recover compressed sensing signals. Ho ever, sometimes 0-norm is an NP problem, 1-norm has no solution in theory and p-norm is not a convex function. We introduce a recovery method of compressed sensing signal based on regularized smooth convex optimization. In order to avoid solving the non-convex optimization problems and no solution condition, a convex function is designed as the objective function of optimization to fit 0-norm of signal and a fast iterative shrinkage-thresholding algorithm is proposed -o find solution with the convergence speed is quadratic convergence. Experimental results showthat our method lias a sound recovery effect and is well suitable for processing big data of compressed CT1 images.
- Subjects
COMPRESSED sensing; THRESHOLDING algorithms; CONVEX functions; COMPUTED tomography; INTERNET stores; SOUNDS
- Publication
Information Technology & Control, 2019, Vol 48, Issue 2, p357
- ISSN
1392-124X
- Publication type
Article
- DOI
10.5755/j01.itc.48.2.21864