Blind deconvolution subject to sparse representation for fluorescence microscopy

Wang, Yu; Dai, Qionghai; Cai, Qiang; Guo, Peiyuan; Liu, Zaiwen

doi:10.1016/j.optcom.2012.07.073

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…There are a number of publications showing the value of L 1 -norm regularized deconvolution in the context of fluorescence microscopic imaging , We highlight the potential of penalized estimation using an L 0 -norm penalty within this area. This method provides subdiffraction limit optical resolution in two dimensions, suitable for achieving subsequent image deconvolution and contrast enhancement.…”

Section: Resultsmentioning

confidence: 99%

Sparse Deconvolution in One and Two Dimensions: Applications in Endocrinology and Single-Molecule Fluorescence Imaging

2014

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Deconvolution of noisy signals is an important task in analytical chemistry, examples being spectral deconvolution or deconvolution in microscopy. When the number of spectral peaks or single emitters in imaging is limited, the solution of the deconvolution is required to be sparse, and desirable results are obtained using a penalized estimation techniques. We impose sparseness by using penalized regression with a penalty based on the L0-norm, as discussed in earlier work. Several extensions to this approach are presented. Results are demonstrated on pulse identification in endocrine data where the aim is to model the secretion pattern as a sparse series of spikes. An application in single-molecule fluorescence imaging demonstrates the algorithm when applied to two-dimensional data.

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Section: Resultsmentioning

confidence: 99%

Sparse Deconvolution in One and Two Dimensions: Applications in Endocrinology and Single-Molecule Fluorescence Imaging

2014

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“…目前人们已经发现, 力学信号由细胞外基质传递到细胞内部主要经由细胞外基质、黏附斑和细胞骨架, 最终到达细胞核以影响基因表达 [14][15][16] . 此外, Qin等人 [17] 建立了一个基于细胞骨架的力学模型, 涉及肌动 [19][20][21][22] . 构建共聚焦点扩散函数的方法通常包括理论推导、微珠实验测试等 [23][24][25][26] .…”

Section: 类的细胞在此过程中细胞微丝结构具有明显区别unclassified

Analyses on cytoskeleton structure basing on confocalmicroscope

Sun¹,

Zhang²,

Bo³

2020

Sci. Sin.-Phys. Mech. Astron.

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“…We can see that is an important parameters group in the process of framelet regularization. The value of group belongs to empirical range [3,10]. The last parameters group is which belongs to empirical range [1,5].…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Poissonian image deconvolution appears in various applications such as astronomical imaging [1], medical imaging [2], and electronic microscope imaging [3]. It aims to reconstruct a high quality image from the degraded image .…”

Section: Introductionmentioning

confidence: 99%

Poissonian Image Deconvolution via Sparse and Redundant Representations and Framelet Regularization

Fang

Wang

2014

Mathematical Problems in Engineering

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Poissonian image deconvolution is a key issue in various applications, such as astronomical imaging, medical imaging, and electronic microscope imaging. A large amount of literature on this subject is analysis-based methods. These methods assign various forward measurements of the image. Meanwhile, synthesis-based methods are another well-known class of methods. These methods seek a reconstruction of the image. In this paper, we propose an approach that combines analysis with synthesis methods. The method is proposed to address Poissonian image deconvolution problem by minimizing the energy functional, which is composed of a sparse representation prior over a learned dictionary, the data fidelity term, and framelet based analysis prior constraint as the regularization term. The minimization problem can be efficiently solved by the split Bregman technique. Experiments demonstrate that our approach achieves better results than many state-of-the-art methods, in terms of both restoration accuracy and visual perception.

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Blind deconvolution subject to sparse representation for fluorescence microscopy

Cited by 6 publications

References 18 publications

Sparse Deconvolution in One and Two Dimensions: Applications in Endocrinology and Single-Molecule Fluorescence Imaging

Sparse Deconvolution in One and Two Dimensions: Applications in Endocrinology and Single-Molecule Fluorescence Imaging

Analyses on cytoskeleton structure basing on confocalmicroscope

Poissonian Image Deconvolution via Sparse and Redundant Representations and Framelet Regularization

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