US20140064615A1 - Method and Device for Denoising Videos Based on Non-Local Means - Google Patents

Method and Device for Denoising Videos Based on Non-Local Means Download PDF

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Publication number
US20140064615A1
US20140064615A1 US14/017,587 US201314017587A US2014064615A1 US 20140064615 A1 US20140064615 A1 US 20140064615A1 US 201314017587 A US201314017587 A US 201314017587A US 2014064615 A1 US2014064615 A1 US 2014064615A1
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pixel
local
histogram
present frame
search
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Jie Ren
Jiaying Liu
Zongming Guo
Yue Zhuo
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Peking University
Peking University Founder Group Co Ltd
Beijing Founder Electronics Co Ltd
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Peking University
Peking University Founder Group Co Ltd
Beijing Founder Electronics Co Ltd
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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
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/40—Image enhancement or restoration using histogram techniques
    • 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
    • G06T2207/10016—Video; Image sequence
    • 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
    • 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
    • G06T2207/20172—Image enhancement details
    • G06T2207/20182—Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering

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  • the present application relates to a method and a device for denoising videos based on non-local means.
  • the present disclosure intends to provide a method and a device for denoising videos based on non-local means to solve the foregoing problems.
  • a local image block centered on a pixel i in a present frame of the video is constructed and then a search area centered on the pixel is delimited as a search window.
  • the respective search window is delimited in same way as the present frame, wherein the constructed search windows constitute a three-dimensional search space. It carries out, in reference to the search window of the present frame, a histogram normalization filtering process of the search windows of remaining frames, so as to obtain a three-dimensional search space with illumination invariance.
  • FIG. 1 depicts a schematic constructive diagram of local image blocks, search windows and a search space according to an embodiment of the present application.
  • FIG. 2 depicts a schematic diagram of a histogram normalization filtering process of a three-dimensional search space according to an embodiment of the present application.
  • FIG. 3 depicts a flowchart of a video denoising process according to an embodiment of the present application.
  • FIG. 4 depicts a schematic diagram of image matching and similarity weight calculation in the filtered three-dimensional search space according to an embodiment of the present application.
  • FIG. 5 depicts a schematic diagram of filtering module according to an embodiment of the present application.
  • a method for denoising videos based on non-local means may remove effects from illumination variances in a video by means of image histogram normalization filtering processes.
  • An embodiment of the present application is proposed in light of the situation where the existing lighting conditions in video image sequences are changed. To be specific, it improves the robustness of searching similar image blocks among frames and computing similarity weights against the changes of lighting conditions by means of image histogram normalization filtering, and thus it denoises the videos based on non-local means with the constant lighting.
  • This embodiment of the present application fills the blanks in video denoising methods against lighting conditions, and may better remove the noise in video sequences, in particular in the case that during shooting videos lighting conditions, such as the effect of flash lamp, change, better improve the robustness of the conventional methods for denoising videos and the visual quality of denoised image.
  • FIG. 3 depicts a flowchart of a video denoising process according to an embodiment of the present application.
  • the process of removing illumination variances in a video by means of image histogram normalization filtering may comprise the following steps.
  • step S 10 a local image block centered on a pixel to be processed in a present frame is constructed and a search area centered on the pixel is delimited as a search window.
  • the corresponding search windows in corresponding areas of other frames are established within a three-dimensional search space, wherein the search windows constitute the three-dimensional search space.
  • step S 20 in reference to or based on the search window of the present frame, a histogram normalization filtering process is carried out on the corresponding search windows of the other frames within the three-dimensional search space to obtain a three-dimensional search space with illumination invariance.
  • the preferred embodiment takes into account the influence on the global and local brightness of the image from the changes of lighting conditions on frame sequences, and eliminate matching errors of image blocks having very similar structure, which are introduced due to the changes of lighting conditions during matching, by using image histogram normalization filtering when the three-dimensional search window is constructed, such that the robustness and accuracy of similarity weight calculation and similarity matching of image blocks are effectively improved, and thus the performance of overall video denoising will be further improved.
  • FIG. 1 depicts a schematic constructive diagram of local image blocks, search windows and search space according to an embodiment of the present application
  • FIG. 2 depicts a schematic diagram of histogram normalization filtering a three-dimensional search space according to an embodiment of the present application.
  • a local image block centered on a pixel i of a frame f n is constructed first.
  • a search window w n centered on the pixel i is constructed, wherein the search window w n is larger than a range of the local image block.
  • Search windows at corresponding positions on each of k frames ahead and behind of frame f n are also constructed so as to obtain a set of search windows ⁇ W n ⁇ k , . . . , w n ⁇ 1 , w n , w n+1 , . . . , w n+k ⁇ which constitute a three-dimensional search space W.
  • step S 20 the image value ⁇ out j of the pixel j after filtering is calculated as follows:
  • ⁇ in j are values of respective pixel j in the corresponding search windows of the other frames within the three-dimensional search space
  • ⁇ out j is the image value of the pixel j after filtering
  • T( ⁇ in ) ⁇ 0 ⁇ in p in (w)dw
  • p in (w) is a probability density of a histogram of ⁇ in j distributed at the luminance level of w
  • the action of this step substantially eliminates the influence of the illumination variances, so a three-dimensional search space with illumination invariance is obtained.
  • step S 30 a similarity weight between the pixels i and j is determined by calculating structural differences between the local image block of f n and all the local image blocks in W.
  • FIG. 4 depicts a schematic diagram of image matching and similarity weight calculation in the filtered three-dimensional search space according to an embodiment of the present application.
  • step S 30 the similarity weight ⁇ (i,j) is calculated as follows:
  • ⁇ ⁇ ( ⁇ , j ) 1 Z ⁇ ( ⁇ ) ⁇ exp ⁇ ( - ⁇ v ⁇ ( B i ) - v ⁇ ( B j ) ⁇ 2 , a 2 h 2 )
  • ⁇ (i,j) is the similarity weight between the pixels i and j
  • B i , B j represent local image blocks centered on the pixels i and j
  • ⁇ (B i ) and ⁇ (B j ) represent vectors constituted by the values of pixels in local image blocks
  • ⁇ • ⁇ 2,a 2 indicates the weighted Euclidean Distance between two vectors, in which symbol a means a spatial weight distribution which conforms to a Gaussian Distribution with its variance of a,
  • h is a designated constant and may be optimized according to different videos for controlling the attributes of weight calculation.
  • step S 40 according to the similarity weight, the pixel i is denoised based on non-local means, for example, by rule of
  • NL[ ⁇ (i)] is the replaced image value of pixel i.
  • a noise-removed image sequence can eventually be obtained and the denoising process of the whole video is completed.
  • a device for denoising a video based on non-local means may include a filtering module 200 for removing illumination variances in a video by means of image histogram normalization filtering. Because this device may remove illumination variances in a video by means of image histogram normalization filtering, so it could make self-adaptive adjustment in response to the changes of lighting conditions.
  • the filtering module 200 may comprise a space module 201 and a histogram module 202 .
  • the space module 201 is configured to construct a local image block which centered on a pixel to be processed in a present frame, to delimit a search area centered on the pixel as a search window, and to establish corresponding search windows in corresponding areas of other frames within a three-dimensional search space, wherein the search windows constitute the three-dimensional search space.
  • the histogram module 202 is configured to carry out a histogram normalization filtering process for the corresponding search windows of the other frames within the three-dimensional search space, so as to obtain a three-dimensional search space with illumination invariance, which is based on or in reference to the search window of the present frame.
  • the space module 201 is used for constructing a local image block centered on a pixel i of a frame f n , constructing a search window w n centered on the pixel i which is larger than a range of the local image block, while constructing one search window at corresponding position on each of k frames ahead of and behind the frame f n , obtaining a set of search windows ⁇ w n ⁇ k , . . . , w n ⁇ 1 , w n , w +1 , . . . , w +k ⁇ which constitute a three-dimensional search space W.
  • ⁇ in j are image values of respective pixel j in the corresponding search windows of the other frames within the three-dimensional search space
  • ⁇ out j is the image value of the pixel j after filtering
  • T( ⁇ in ) ⁇ 0 ⁇ in p in (w)dw
  • p in (w) is a probability density of a histogram of ⁇ in j distributed at the luminance level of w
  • the present application may make better use of the time domain consistency between video image sequences and be less influenced by the changes of lighting conditions by means of image histogram normalization filtering, thus the recovering quality and the robustness of video denoising are improved.
  • modules or steps of the present application may be implemented with a common computing device.
  • the modules or steps of the present application can be concentrated or run in a single computing device or distributed in a network composed of multiple computing devices.
  • the modules or steps may be achieved by using codes of the executable program, so that they can be stored in the storage medium and be run by one or more processors in the device, or the plurality of the modules or steps can be fabricated into an individual integrated circuit module. Therefore, the present application is not limited to any particular hardware, software or combination thereof.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
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Cited By (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140118578A1 (en) * 2012-10-25 2014-05-01 Canon Kabushiki Kaisha Image processing apparatus and image processing method
US20140226905A1 (en) * 2013-02-14 2014-08-14 Canon Kabushiki Kaisha Image processing apparatus, image processing method, and storage medium
CN104599250A (zh) * 2015-01-23 2015-05-06 河南理工大学 基于局部均值差值的自适应伪球滤波方法
CN104616259A (zh) * 2015-02-04 2015-05-13 西安理工大学 一种噪声强度自适应的非局部均值图像去噪方法
WO2016048464A1 (fr) * 2014-09-23 2016-03-31 Intel Corporation Débruitage d'image par moyennes non locales avec conservation des détails en utilisant le mélange piloté par autosimilitude
US10043252B2 (en) 2016-06-14 2018-08-07 Intel Corporation Adaptive filtering with weight analysis
CN109785246A (zh) * 2018-12-11 2019-05-21 深圳奥比中光科技有限公司 一种非局部均值滤波的降噪方法、装置及设备
CN110163825A (zh) * 2019-05-23 2019-08-23 大连理工大学 一种人类胚胎心脏超声图像去噪和增强方法
CN110619609A (zh) * 2019-09-09 2019-12-27 上海海事大学 一种基于时空非局部相似性与hosvd投影的红外dtdi成像方法
CN112053291A (zh) * 2020-07-20 2020-12-08 清华大学 基于深度学习的弱光视频降噪方法及装置
CN113759375A (zh) * 2021-09-15 2021-12-07 云南师范大学 基于统计特性的sar图像非局部均值去噪方法
CN113934715A (zh) * 2021-09-25 2022-01-14 西北工业大学 一种基于改进非局部均值的推进剂制备质量数据去噪方法
EP3895118A4 (fr) * 2019-01-17 2022-02-02 Zhejiang Dahua Technology Co., Ltd Systèmes et procédés de réduction de bruit
US11282173B2 (en) 2013-02-14 2022-03-22 Canon Kabushiki Kaisha Image processing apparatus, image processing method, and storage medium for reducing noise in a captured image
CN115049614A (zh) * 2022-06-14 2022-09-13 南京航空航天大学 一种基于改进nlm算法的rfid标签图像去噪方法
CN116258649A (zh) * 2023-03-09 2023-06-13 吉林农业科技学院 基于熔池状态分析的焊接参数自适应调整方法
CN116385416A (zh) * 2023-04-11 2023-07-04 杭州三普机械有限公司 一种高速编织机智能控制方法及系统
CN117237232A (zh) * 2023-11-10 2023-12-15 山东天意机械股份有限公司 基于图像去噪的建材生产环境粉尘在线监测方法
CN117788570A (zh) * 2024-02-26 2024-03-29 山东济矿鲁能煤电股份有限公司阳城煤矿 一种基于机器视觉的斗轮机定位方法及系统

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104010114B (zh) * 2014-05-29 2017-08-29 广东威创视讯科技股份有限公司 视频去噪方法和装置
WO2016149867A1 (fr) * 2015-03-20 2016-09-29 Mediatek Singapore Pte. Ltd. Prédiction non locale pour le codage par palettes
CN105184741A (zh) * 2015-08-03 2015-12-23 山东师范大学 基于改进非局部均值的三维cbct图像除噪方法
CN105554447B (zh) * 2015-12-11 2018-10-23 北京天地玛珂电液控制系统有限公司 一种基于图像处理技术的采煤工作面实时视频拼接系统
US10282831B2 (en) * 2015-12-28 2019-05-07 Novatek Microelectronics Corp. Method and apparatus for motion compensated noise reduction
US20190045224A1 (en) * 2016-02-04 2019-02-07 Mediatek Inc. Method and apparatus of non-local adaptive in-loop filters in video coding
CN106485679B (zh) * 2016-10-12 2019-07-09 北京大学 一种数据驱动的自适应视频图像去噪方法
EP3352454B1 (fr) 2017-01-19 2019-01-02 Axis AB Procédé de réduction de bruit de motif fixe et utilisation d'un tel procédé
CN110288540B (zh) * 2019-06-04 2021-07-06 东南大学 一种碳纤维导线x射线图像在线成像标准化方法
CN110570374B (zh) * 2019-09-05 2022-04-22 湖北南邦创电科技有限公司 一种对红外传感器所获得图像的处理方法
CN112085102B (zh) * 2020-09-10 2023-03-10 西安电子科技大学 基于三维时空特征分解的无参考视频质量评价方法
CN118014886B (zh) * 2024-04-10 2024-06-21 中建安装集团有限公司 一种超声波管道裂纹图像增强方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100141804A1 (en) * 2004-05-05 2010-06-10 Centre National De La Recherche Scientifique-Cnrs Image data processing method by reducing image noise, and camera integrating means for implementing said method
US8170360B2 (en) * 2008-11-21 2012-05-01 Drs Rsta, Inc. Method and system for enhancing images using multi-resolution histogram shaping
US20120155764A1 (en) * 2010-12-15 2012-06-21 Sony Corporation Image processing device, image processing method and program

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR0176601B1 (ko) * 1996-05-21 1999-05-01 김광호 저역 필터링과 히스토그램 등화를 이용한 화질개선 방법 및 그 회로
US9354490B2 (en) * 2009-01-09 2016-05-31 New York University Method, computer-accessible, medium and systems for facilitating dark flash photography
CN101777129B (zh) * 2009-11-25 2012-05-23 中国科学院自动化研究所 一种基于特征检测的图像匹配方法
CN101833753B (zh) * 2010-04-30 2012-05-09 西安电子科技大学 基于改进贝叶斯非局部均值滤波器的sar图像去斑方法
JP5606625B2 (ja) * 2010-07-21 2014-10-15 ドルビー ラボラトリーズ ライセンシング コーポレイション ビデオ符号化のための高度な動きモデルを使った参照処理
MX2013002429A (es) * 2010-09-03 2013-04-08 Dolby Lab Licensing Corp Metodo y sistema para compensacion de iluminacion y transicion para codificacion y procesamiento de video.
US8928813B2 (en) * 2010-10-28 2015-01-06 Microsoft Corporation Methods and apparatus for reducing structured noise in video
CN102184552B (zh) * 2011-05-11 2013-06-26 上海理工大学 一种基于差分融合与图像边缘信息的运动目标检测方法
CN102438107B (zh) * 2011-08-09 2013-11-13 宁波大学 一种显微视频图像的处理方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100141804A1 (en) * 2004-05-05 2010-06-10 Centre National De La Recherche Scientifique-Cnrs Image data processing method by reducing image noise, and camera integrating means for implementing said method
US8170360B2 (en) * 2008-11-21 2012-05-01 Drs Rsta, Inc. Method and system for enhancing images using multi-resolution histogram shaping
US20120155764A1 (en) * 2010-12-15 2012-06-21 Sony Corporation Image processing device, image processing method and program

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
A. Buades, B. Coll, and J. M. Morel, Denoising Image Sequences Does Not Require Motion Estimation, 2005 *
J. Ren, Y. Zhuo, J. Liu, and Z. Guo. "Illumination-Invariant Non-Local Means based Video Denoising". International Conference on Image Processing, Sep. 2012. *
L. Yang , R. Parton , G. Ball , Z. Qiu , A. H. Greenaway , I. Davis and W. Lu "An adaptive non-local means filter for denoising live-cell images and improving particle detection", J. Struct. Biol., vol. 172, no. 3, pp.233 -243 2010 *
Ren, J. et al. "Illumination-Invariant Non-Local Means Based Video Denoising". Peking University, May 06 2012. Web. Accessed January 16 2014. *

Cited By (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9818176B2 (en) * 2012-10-25 2017-11-14 Canon Kabushiki Kaisha Image processing apparatus and image processing method
US10515437B2 (en) 2012-10-25 2019-12-24 Canon Kabushiki Kaisha Image processing apparatus and image processing method that perform noise reduction processing on image data
US20140118578A1 (en) * 2012-10-25 2014-05-01 Canon Kabushiki Kaisha Image processing apparatus and image processing method
US20170316550A1 (en) * 2013-02-14 2017-11-02 Canon Kabushiki Kaisha Image processing apparatus, image processing method, and storage medium
US10504212B2 (en) * 2013-02-14 2019-12-10 Canon Kabushiki Kaisha Image processing apparatus, image processing method, and storage medium
US9384531B2 (en) * 2013-02-14 2016-07-05 Canon Kabushiki Kaisha Image processing apparatus, image processing method, and storage medium for reducing noise in a captured image
US11282173B2 (en) 2013-02-14 2022-03-22 Canon Kabushiki Kaisha Image processing apparatus, image processing method, and storage medium for reducing noise in a captured image
US9747671B2 (en) 2013-02-14 2017-08-29 Canon Kabushiki Kaisha Image processing apparatus, image processing method, and storage medium
US20140226905A1 (en) * 2013-02-14 2014-08-14 Canon Kabushiki Kaisha Image processing apparatus, image processing method, and storage medium
WO2016048464A1 (fr) * 2014-09-23 2016-03-31 Intel Corporation Débruitage d'image par moyennes non locales avec conservation des détails en utilisant le mélange piloté par autosimilitude
US9489720B2 (en) 2014-09-23 2016-11-08 Intel Corporation Non-local means image denoising with detail preservation using self-similarity driven blending
CN104599250A (zh) * 2015-01-23 2015-05-06 河南理工大学 基于局部均值差值的自适应伪球滤波方法
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US10043252B2 (en) 2016-06-14 2018-08-07 Intel Corporation Adaptive filtering with weight analysis
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EP3895118A4 (fr) * 2019-01-17 2022-02-02 Zhejiang Dahua Technology Co., Ltd Systèmes et procédés de réduction de bruit
US11341618B2 (en) 2019-01-17 2022-05-24 Zhejiang Dahua Technology Co., Ltd. Systems and methods for noise reduction
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