CN110313016A - 一种基于稀疏正源分离模型的图像去模糊算法 - Google Patents

一种基于稀疏正源分离模型的图像去模糊算法 Download PDF

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CN110313016A
CN110313016A CN201780068103.3A CN201780068103A CN110313016A CN 110313016 A CN110313016 A CN 110313016A CN 201780068103 A CN201780068103 A CN 201780068103A CN 110313016 A CN110313016 A CN 110313016A
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image
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imaging
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CN110313016B (zh
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林倞
陈崇雨
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National Sun Yat Sen University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/73—Deblurring; Sharpening
    • G—PHYSICS
    • G02—OPTICS
    • G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00—Microscopes
    • G02B21/0004—Microscopes specially adapted for specific applications
    • G02B21/002—Scanning microscopes
    • G02B21/0024—Confocal scanning microscopes (CSOMs) or confocal "macroscopes"; Accessories which are not restricted to use with CSOMs, e.g. sample holders
    • G02B21/008—Details of detection or image processing, including general computer control
    • G—PHYSICS
    • G02—OPTICS
    • G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00—Microscopes
    • G02B21/36—Microscopes arranged for photographic purposes or projection purposes or digital imaging or video purposes including associated control and data processing arrangements
    • G02B21/365—Control or image processing arrangements for digital or video microscopes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20021—Dividing image into blocks, subimages or windows
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Optics & Photonics (AREA)
  • General Engineering & Computer Science (AREA)
  • Image Processing (AREA)

Abstract

一种基于稀疏正源分离模型的图像去模糊算法，用于对光学显微成像系统采集到的因衍射效应与光学偏差而产生的模糊图像进行处理，可以在单次感光成像并且不增加外部成像设备的情况下，将光学显微系统的空间分辨率提升到纳米量级。在所述方法中，显微成像的模糊过程被表示为成像系统点扩散函数的线性组合。将该过程嵌入正源分离的优化框架中，对其加入稀疏性约束并求解以去除模糊，从而实现高分辨显微成像。所述方法还包括对实际光学显微镜的预处理步骤，通过去除显微图像中的背景散射等干扰，使得输入的模糊图像更符合所提的成像模型。有关实验表明，将单个模糊显微图像作为输入，所述方法取得了比其他方法更好的细节解析性能。

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PCT国内申请，说明书已公开。

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  1. PCT国内申请，权利要求书已公开。
CN201780068103.3A 2017-06-15 2017-06-15 一种基于稀疏正源分离模型的图像去模糊算法 Active CN110313016B (zh)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111310903A (zh) * 2020-02-24 2020-06-19 清华大学 基于卷积神经网络的三维单分子定位系统
CN111462005A (zh) * 2020-03-30 2020-07-28 腾讯科技（深圳）有限公司 处理显微图像的方法、装置、计算机设备及存储介质
CN116337830A (zh) * 2023-03-07 2023-06-27 清华大学 基于衍射极限尺寸小孔的双光子合成孔径成像方法及装置

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10761419B2 (en) * 2017-04-17 2020-09-01 Washington University Systems and methods for performing optical imaging using a tri-spot point spread function (PSF)
US11615510B2 (en) * 2020-10-21 2023-03-28 Samsung Electronics Co., Ltd. Kernel-aware super resolution
CN113191981B (zh) * 2021-05-13 2022-09-09 清华大学 基于非负矩阵分解的深层双光子钙成像方法和装置
CN113379647A (zh) * 2021-07-08 2021-09-10 湘潭大学 一种优化psf估计的多特征图像复原方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105046659A (zh) * 2015-07-02 2015-11-11 中国人民解放军国防科学技术大学 一种基于稀疏表示的单透镜计算成像psf估算方法
CN105809642A (zh) * 2016-03-11 2016-07-27 中山大学 一种基于l0正则化的自然图像盲去运动模糊的方法
CN105954750A (zh) * 2016-04-29 2016-09-21 清华大学 基于压缩感知的条带式合成孔径雷达非稀疏场景成像方法
US20160335747A1 (en) * 2015-04-22 2016-11-17 Adobe Systems Incorporated Scale adaptive blind deblurring
CN106204472A (zh) * 2016-06-30 2016-12-07 北京大学 基于稀疏特性的视频图像去模糊方法

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007041383A2 (en) * 2005-09-30 2007-04-12 Purdue Research Foundation Endoscopic imaging device
WO2007061632A2 (en) * 2005-11-09 2007-05-31 Geometric Informatics, Inc. Method and apparatus for absolute-coordinate three-dimensional surface imaging
WO2012084726A1 (en) * 2010-12-21 2012-06-28 Deutsches Krebsforschungszentrum Method and system for 4d radiological intervention guidance
US9430854B2 (en) * 2012-06-23 2016-08-30 Wisconsin Alumni Research Foundation System and method for model consistency constrained medical image reconstruction
DE102012211662B4 (de) * 2012-07-04 2015-01-08 Bruker Biospin Mri Gmbh Kalibrierverfahren für eine MPI (=Magnetic-Particle-Imaging)-Apparatur
CN103576137B (zh) * 2013-09-27 2015-05-27 电子科技大学 一种基于成像策略的多传感器多目标定位方法
US9734601B2 (en) * 2014-04-04 2017-08-15 The Board Of Trustees Of The University Of Illinois Highly accelerated imaging and image reconstruction using adaptive sparsifying transforms
CN106373105B (zh) * 2016-09-12 2020-03-24 广东顺德中山大学卡内基梅隆大学国际联合研究院 基于低秩矩阵恢复的多曝光图像去伪影融合方法
CN106530261B (zh) * 2016-12-28 2019-03-19 同观科技（深圳）有限公司 一种双动态模糊图像复原方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160335747A1 (en) * 2015-04-22 2016-11-17 Adobe Systems Incorporated Scale adaptive blind deblurring
CN105046659A (zh) * 2015-07-02 2015-11-11 中国人民解放军国防科学技术大学 一种基于稀疏表示的单透镜计算成像psf估算方法
CN105809642A (zh) * 2016-03-11 2016-07-27 中山大学 一种基于l0正则化的自然图像盲去运动模糊的方法
CN105954750A (zh) * 2016-04-29 2016-09-21 清华大学 基于压缩感知的条带式合成孔径雷达非稀疏场景成像方法
CN106204472A (zh) * 2016-06-30 2016-12-07 北京大学 基于稀疏特性的视频图像去模糊方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
VENIAMIN I. MORGENSHTERN 等: "Super-Resolution of Positive Sources: the Discrete Setup", 《SIAM JOURNAL ON IMAGING SCIENCES》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111310903A (zh) * 2020-02-24 2020-06-19 清华大学 基于卷积神经网络的三维单分子定位系统
CN111462005A (zh) * 2020-03-30 2020-07-28 腾讯科技（深圳）有限公司 处理显微图像的方法、装置、计算机设备及存储介质
CN116337830A (zh) * 2023-03-07 2023-06-27 清华大学 基于衍射极限尺寸小孔的双光子合成孔径成像方法及装置
CN116337830B (zh) * 2023-03-07 2024-03-26 清华大学 基于衍射极限尺寸小孔的双光子合成孔径成像方法及装置

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US10937131B2 (en) 2021-03-02
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US20190318459A1 (en) 2019-10-17

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