The application of compressed sensing reconstruction algorithms for MRI of glioblastoma

Zhang, Haowei; Ren, Xiaoqian; Liu, Ying; Zhou, Qixin

doi:10.1109/cisp-bmei.2017.8302072

Cited by 2 publications

(1 citation statement)

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“…Image decomposition plays a fundamental role in image processing and computer vision, such as image inpainting [1], pattern recognition [2,3], biomedical engineering [4,5,6], and many others. Generally, image decomposition refers to the task of decomposing a natural image into a so-called cartoon part corresponding to the geometrical part or sketchy approximation of the image, and a texture component characterizing the oscillating part or small scale special patterns of the image.…”

Section: Introductionmentioning

confidence: 99%

Low patch-rank image decomposition using alternating minimization algorithms

2021

J. Nonlinear Var. Anal.

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Cartoon-texture image decomposition, which refers to the problem of decomposing an image into a cartoon part and a texture component, is one of the most fundamental problems in image processing. In this paper, we are concerned with the low patch-rank enhanced image decomposition model, which is a convex but nonsmooth optimization problem that could not be solved directly by traditional gradient-based optimization algorithms. Accordingly, we introduce two unconstrained reformulations to the underlying low patch-rank optimization model. Furthermore, by exploiting favorable structures of the resulting reformations, we propose two easily implementable alternating minimization algorithms, whose subproblems have closed-form solutions. Compared to the state-of-the-art multi-block alternating direction method of multipliers and its variants, our proposed algorithms enjoy simpler iterative schemes and lower memory requirements for saving computing time. A series of numerical experiments further support the promising performance of the proposed approaches.

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