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 PDFInfo
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- 238000003384 imaging method Methods 0.000 claims abstract description 43
- 238000000034 method Methods 0.000 claims abstract description 40
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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
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- G—PHYSICS
- G02—OPTICS
- G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
- G02B21/00—Microscopes
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- G02B21/008—Details of detection or image processing, including general computer control
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- 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
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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
- G06T5/20—Image enhancement or restoration using local operators
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- 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
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- 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
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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***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***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***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。
Description
PCT国内申请,说明书已公开。
Claims (5)
- PCT国内申请,权利要求书已公开。
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Cited By (3)
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*** |
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)
Publication number | Priority date | Publication date | Assignee | Title |
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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 |
CN113379647B (zh) * | 2021-07-08 | 2024-07-02 | 湘潭大学 | 一种优化psf估计的多特征图像复原方法 |
Citations (5)
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)
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 |
-
2017
- 2017-06-15 CN CN201780068103.3A patent/CN110313016B/zh active Active
- 2017-06-15 US US16/341,841 patent/US10937131B2/en active Active
- 2017-06-15 WO PCT/CN2017/088413 patent/WO2018227465A1/zh active Application Filing
Patent Citations (5)
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)
Title |
---|
VENIAMIN I. MORGENSHTERN 等: "Super-Resolution of Positive Sources: the Discrete Setup", 《SIAM JOURNAL ON IMAGING SCIENCES》 * |
Cited By (4)
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*** |
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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WO2018227465A1 (zh) | 2018-12-20 |
US20190318459A1 (en) | 2019-10-17 |
CN110313016B (zh) | 2023-08-15 |
US10937131B2 (en) | 2021-03-02 |
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