CN113378415B - Multimedia data self-adaptive recovery method and device based on local and global constraints - Google Patents

Multimedia data self-adaptive recovery method and device based on local and global constraints Download PDF

Info

Publication number
CN113378415B
CN113378415B CN202110925533.0A CN202110925533A CN113378415B CN 113378415 B CN113378415 B CN 113378415B CN 202110925533 A CN202110925533 A CN 202110925533A CN 113378415 B CN113378415 B CN 113378415B
Authority
CN
China
Prior art keywords
data
noise
local
global
representing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110925533.0A
Other languages
Chinese (zh)
Other versions
CN113378415A (en
Inventor
郭丽
刘知贵
张小乾
李理
付聪
吴均
喻琼
张活力
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southwest University of Science and Technology
Original Assignee
Southwest University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southwest University of Science and Technology filed Critical Southwest University of Science and Technology
Priority to CN202110925533.0A priority Critical patent/CN113378415B/en
Publication of CN113378415A publication Critical patent/CN113378415A/en
Application granted granted Critical
Publication of CN113378415B publication Critical patent/CN113378415B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/10—Noise analysis or noise optimisation

Abstract

The invention discloses a multimedia data self-adaptive recovery method and a device based on local and global constraints, wherein the method comprises the following steps: extracting characteristic data of the multimedia data; carrying out low-rank constraint on the feature data by using the constructed data global constraint model; extracting noise in the characteristic data by using the constructed noise data local similarity measurement model; and iteratively solving the data global constraint model and the noise data local similarity measurement model by using an alternating minimization method or an alternating direction multiplier method to obtain clean data and noise data. The invention aims to provide a multimedia data self-adaptive recovery method and device based on local and global constraints, which can improve the data recovery quality, especially have more obvious recovery effect on seriously damaged images, videos and other multimedia data, can provide more accurate image and video target and background information, and achieve more ideal denoising effect.

Description

Multimedia data self-adaptive recovery method and device based on local and global constraints
Technical Field
The invention relates to the technical field of data recovery, in particular to a multimedia data self-adaptive recovery method and device based on local and global constraints.
Background
Multimedia data such as images, videos and audios provide a large amount of colorful information for daily life and work, but the multimedia data structures often lead to a large amount of noise caused by data collectors, natural conditions, human factors and the like, so that the essential structure of the data is damaged, and certain difficulty is caused for data processing. For example, image data is often interfered by different light changes, occlusion, image corruption, gaussian noise and non-gaussian noise in practical applications; video data is often introduced with non-gaussian noise such as loss, destruction, outliers, and the like of motion trajectory features due to low acquisition frequency, mutual occlusion between moving bodies, self-occlusion of moving bodies, and the like.
At present, in order to deal with such noise present in multimedia, a Robust Principal Component Analysis (RPCA) is designed and used. The RPCA utilizes the low-rank property of clean data and the sparseness of noise data, and respectively carries out low-rank constraint and noise modeling on the clean data and the noise data, thereby realizing the separation of the clean data and the noise from the noise-containing data. Generally, the RPCA method can obtain a good effect when recovering data damaged by light shielding and gaussian noise, but has a poor data recovery effect when the data is seriously damaged and the shielding surface is large.
Disclosure of Invention
The invention aims to provide a multimedia data self-adaptive recovery method and device based on local and global constraints, which can improve the data recovery quality, especially have more obvious recovery effect on seriously damaged images, videos and other multimedia data, can provide more accurate image and video target and background information, and achieve more ideal denoising effect.
The invention is realized by the following technical scheme:
extracting feature data of the multimedia data, wherein the feature data comprise global features and local features;
carrying out low-rank constraint on the feature data by using the constructed data global constraint model;
the data global constraint model is as follows:
Figure GDA0003282509250000011
wherein the content of the first and second substances,
Figure GDA0003282509250000012
representing a clean data matrix, E representing a noisy data matrix, | A | | luminancew,*Represents a weighted nuclear norm, Tr (C (wA) DT) Representing matrices C (wA) DTThe trace of (a) is determined,
Figure GDA0003282509250000013
representing the characteristic data, C and D are unitary matrixes obtained by performing singular value decomposition on A,
Figure GDA0003282509250000021
the expression that the singular values of the data a are truncated and weighted is low-rank constrained, namely: firstly, removing the first r larger eigenvalues, then endowing different weights to the rest singular values according to different importance of the singular values, wherein I represents an identity matrix, and r represents that the first r larger singular values are removed after the singular values are sequenced from large to small;
extracting noise in the characteristic data by using the constructed noise data local similarity measurement model;
the noise data local similarity measurement model is as follows:
Figure GDA0003282509250000022
wherein, gσ(. is a Gaussian kernel function, and
Figure GDA0003282509250000023
σ denotes the nuclear size, EiRow i of the matrix E of noisy data
And (3) iteratively solving equations (1) and (2) by using an alternating minimization method or an alternating direction multiplier method to obtain clean data and noise data.
The multimedia data self-adaptive recovery device based on local and global constraints comprises a control module, a data acquisition module, a data processing module and a data display module;
the control module is used for controlling the working processes of the data acquisition module, the data processing module and the data display module;
the data acquisition module is used for acquiring multimedia data to be processed;
the data processing module is used for processing the multimedia data according to the multimedia data self-adaptive recovery method based on the local constraint and the global constraint;
and the data display module is used for displaying the video, image or audio data processed by the data processing module.
Preferably, the data processing module comprises an extraction unit, a low rank constraint unit, a noise extraction unit and a calculation unit;
the extraction unit is used for extracting feature data of the multimedia data, and the feature data comprises global features and local features;
the low-rank constraint unit is used for performing low-rank constraint on the feature data by using the constructed data global constraint model;
the data global constraint model is as follows:
Figure GDA0003282509250000024
wherein the content of the first and second substances,
Figure GDA0003282509250000025
representing a clean data matrix, E-tableRepresenting a matrix of noisy data, | A | | non-woven phosphorw,*Represents a weighted nuclear norm, Tr (C (wA) DT) Representing matrices C (wA) DTThe trace of (a) is determined,
Figure GDA0003282509250000031
representing the characteristic data, C and D are unitary matrixes obtained by performing singular value decomposition on A,
Figure GDA0003282509250000032
the expression that the singular values of the data a are truncated and weighted is low-rank constrained, namely: firstly, removing the first r larger eigenvalues, then endowing different weights to the rest singular values according to different importance of the singular values, wherein I represents an identity matrix, and r represents that the first r larger singular values are removed after the singular values are sequenced from large to small;
the noise extraction unit is used for extracting noise in the feature data by using the constructed noise data local similarity measurement model;
the noise data local similarity measurement model is as follows:
Figure GDA0003282509250000033
wherein, gσ(. is a Gaussian kernel function, and
Figure GDA0003282509250000034
σ denotes the nuclear size, EiRow i representing the noisy data matrix E;
and the computing unit is used for solving the formula (1) and the formula (2) iteratively by using an alternating minimization method or an alternating direction multiplier method so as to obtain clean data and noise data.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the data recovery quality can be improved, particularly, the recovery effect on seriously damaged multimedia data such as images, videos and the like is more obvious, more accurate image and video target and background information can be provided, and a more ideal denoising effect is achieved;
2. the sum of all singular values is not minimized for the truncated kernel norm, and only the smaller min (m, n) -r singular values are minimized, so that the interference generated by the larger singular values is avoided, and the global structure of the feature data is better mined.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
fig. 1 is a schematic diagram of video data recovery using the adaptive recovery method provided by the present invention;
fig. 2 is a block diagram of an adaptive recovery apparatus according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
The embodiment provides a multimedia data self-adaptive recovery method based on local and global constraints, which comprises the following steps:
extracting characteristic data of the multimedia data;
the feature data in this embodiment includes global features (e.g., color features, texture features, shape features, spatial relationship features, or optical flow features of an image, where the color features include color histograms and color sets) and local features (including edges, corners, lines, curves, and regions of special attributes, etc.);
since the global features are insensitive to the changes of the direction, the size and the like of the image or the image area and cannot well capture the local feature description of the object in the image, the global features of the multimedia data are not suitable for processing the situations of image mixture and occlusion, and the local features are the features extracted from the local area of the image and comprise edges, corners, lines, curves, special attribute areas and the like.
It should be noted that how to extract feature data from multimedia data is the prior art, and therefore this application does not describe it, and the improvement point of this step is: in the prior art, most of the images are subjected to denoising processing by only extracting global features, and the scheme combines the global features and the local features and adopts a multi-mode of combining the global features and the local features to perform denoising processing on the images.
Carrying out low-rank constraint on the characteristic data by using the constructed data global constraint model to obtain clean data;
generally, the clean data matrix has a low-rank characteristic, and people often perform low-rank constraint on data in the field of data processing to mine a potential low-rank structure of the data. For example, the RPCA data recovery method and its extension method both use a standard kernel norm to perform low-rank constraint on data, and when this constraint mode performs low-rank constraint on noisy data, the rank components of the data matrix are treated the same, i.e. the influence of larger rank components and their sensitivity to noise are not considered, and it is difficult to implement low-rank representation in practical application. Therefore, in the scheme, in order to better utilize the prior characteristics of the observed data, the data is subjected to low-rank constraint by using the enhanced truncated rank weighted norm, that is: the first r larger eigenvalues are removed first, and then different weights are given to the remaining singular values according to different importance of the singular values. The method comprises the following steps:
the first step is as follows: and (3) truncating the first r larger singular values, wherein when the data matrix has a low-rank structure, noise is contained in relatively small eigenvalues, and the error of the noise is controlled by using the sum of the small eigenvalues, so that the low-rank characteristic of the data is better represented.
The second step is that: and (4) distributing the weight of the eigenvalue, endowing different weights according to the importance of the eigenvalue components, endowing the eigenvalue with higher importance with a larger weight, and endowing a smaller weight with a smaller singular value of the eigenvalue.
Specifically, the data global constraint model in the scheme is as follows:
Figure GDA0003282509250000041
wherein the content of the first and second substances,
Figure GDA0003282509250000051
representing a clean data matrix, E representing a noisy data matrix, | A | | luminancew,*Represents a weighted nuclear norm, Tr (C (wA) DT) Representing matrices C (wA) DTThe trace of (a) is determined,
Figure GDA0003282509250000052
representing the characteristic data, C and D are unitary matrixes obtained by performing singular value decomposition on A,
Figure GDA0003282509250000053
the expression that the singular values of the data a are truncated and weighted is low-rank constrained, namely: firstly, removing the first r larger eigenvalues, then endowing different weights to the rest singular values according to different importance of the singular values, wherein I represents an identity matrix, and r represents that the first r larger singular values are removed after the singular values are sequenced from large to small;
the data global constraint model in the scheme is a non-convex model, in order to solve the model accurately at high speed, the scheme adopts an alternate minimization method or an alternate direction multiplier method to solve low-rank components contained in the multimedia data, and the low-rank components correspond to the de-noised clean data.
The method for solving the low-rank component contained in the multimedia data by adopting an alternating minimization method or an alternating direction multiplier method is the prior art, so the specific solving process is not explained; the improvement point of the step is that: in the prior art, the standard nuclear norm is mostly adopted to carry out low-rank constraint on data, and the enhanced truncation rank weighting nuclear norm is adopted to carry out low-rank constraint on the data in the scheme.
Extracting noise in the characteristic data by using the constructed noise data local similarity measurement model to obtain noise data;
in the existing RPCA (robust principal component analysis) and extension methods, the L1 norm (| | E | | tory) is often adopted1) Modeling data noise is generally better for linear data recovery, but it is less effective for noise modeling and data recovery when dealing with non-linear data (e.g., complex noise). In the application, most of the extracted feature data is nonlinear data, and the noise data in the multimedia data cannot be effectively removed by adopting the conventional RPCA (robust principal component analysis) and expansion method. Compared with the traditional sparse representation (I E I non woven calculation)1) Compared with the denoising method, the noise data local similarity measurement model has a better data recovery effect, and can obtain higher-quality denoising data, so that the reliability of subsequent multimedia signal processing and analysis is effectively guaranteed. Specifically, the noise data local similarity metric model of the present application is shown as follows:
Figure GDA0003282509250000054
wherein, gσ(. is a Gaussian kernel function, and
Figure GDA0003282509250000061
σ denotes the nuclear size, EiRepresenting the ith row of the matrix of noise data E.
In summary, in the present application, noise data and clean data are classified by iteratively solving equations (1) and (2) by using an alternating minimization method or an alternating direction multiplier method, so as to obtain recovered clean data and noise data, as shown in fig. 1.
Example 2
The embodiment provides a multimedia data self-adaptive recovery device based on local and global constraints, which comprises a control module, a data acquisition module, a data processing module and a data display module;
in this embodiment, as shown in fig. 2, the control module is configured as a microcomputer and is mainly responsible for sending signal commands and controlling terminals, that is: the data acquisition module is used for acquiring data and transmitting the data to the data processing module;
the data acquisition module is used for acquiring multimedia data to be processed;
the data processing module is used for processing the multimedia data according to the multimedia data self-adaptive recovery method based on the local constraint and the global constraint provided by the embodiment 1, removing noise data and recovering clean data;
and the data display module is used for converting the recovered clean data into corresponding video, image and audio data according to the requirements of the user and displaying the video, image and audio data on the user terminal.
Specifically, the data processing module of the present embodiment includes an extraction unit, a low rank constraint unit, an adaptive learning unit, a noise extraction unit, and a calculation unit;
the extraction unit is used for extracting feature data of the multimedia data, and the feature data comprises global features and local features;
the low-rank constraint unit is used for carrying out low-rank constraint on the characteristic data by using the constructed data global constraint model;
the data global constraint model is as follows:
Figure GDA0003282509250000062
wherein the content of the first and second substances,
Figure GDA0003282509250000063
representing a clean data matrix, E representing a noisy data matrix, | A | | luminancew,*Represents a weighted nuclear norm, Tr (C (wA) DT) Representing matrices C (wA) DTThe trace of (a) is determined,
Figure GDA0003282509250000064
representing the characteristic data, C and D are unitary matrixes obtained by performing singular value decomposition on A,
Figure GDA0003282509250000065
the expression that the singular values of the data a are truncated and weighted is low-rank constrained, namely: firstly, removing the first r larger eigenvalues, then endowing different weights to the rest singular values according to different importance of the singular values, wherein I represents an identity matrix, and r represents that the first r larger singular values are removed after the singular values are sequenced from large to small;
the noise extraction unit is used for extracting noise in the characteristic data by using the constructed noise data local similarity measurement model;
the noise data local similarity measurement model is as follows:
Figure GDA0003282509250000071
wherein, gσ(. is a Gaussian kernel function, and
Figure GDA0003282509250000072
σ denotes the nuclear size, EiRow i representing the noisy data matrix E;
and the computing unit is used for iteratively solving the expression (1) and the expression (2) by using an alternative minimization method or an alternative direction multiplier method so as to obtain clean data and noise data.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (3)

1. The method for adaptively recovering the multimedia data based on the local constraint and the global constraint is characterized by comprising the following steps of:
extracting feature data of the multimedia data, wherein the feature data comprise global features and local features;
carrying out low-rank constraint on the feature data by using the constructed data global constraint model;
the data global constraint model is as follows:
Figure FDA0003282509240000011
wherein the content of the first and second substances,
Figure FDA0003282509240000012
representing a clean data matrix, E representing a noisy data matrix, | A | | luminancew,*Represents a weighted nuclear norm, Tr (C (wA) DT) Representing matrices C (wA) DTThe trace of (a) is determined,
Figure FDA0003282509240000013
representing the characteristic data, C and D are unitary matrixes obtained by performing singular value decomposition on A,
Figure FDA0003282509240000014
the expression that the singular values of the data a are truncated and weighted is low-rank constrained, namely: firstly, removing the first r larger eigenvalues, then endowing different weights to the rest singular values according to different importance of the singular values, wherein I represents an identity matrix, and r represents that the first r larger singular values are removed after the singular values are sequenced from large to small;
extracting noise in the characteristic data by using the constructed noise data local similarity measurement model;
the noise data local similarity measurement model is as follows:
Figure FDA0003282509240000015
wherein, gσ(. is a Gaussian kernel function, and
Figure FDA0003282509240000016
σ denotes the nuclear size, EiRow i representing the noisy data matrix E;
and (3) iteratively solving equations (1) and (2) by using an alternating minimization method or an alternating direction multiplier method to obtain clean data and noise data.
2. The multimedia data self-adaptive recovery device based on local and global constraints is characterized by comprising a control module, a data acquisition module, a data processing module and a data display module;
the control module is used for controlling the working processes of the data acquisition module, the data processing module and the data display module;
the data acquisition module is used for acquiring multimedia data to be processed;
the data processing module is used for processing the multimedia data according to the multimedia data adaptive recovery method based on the local and global constraints as claimed in claim 1;
and the data display module is used for displaying the multimedia data processed by the data processing module.
3. The adaptive multimedia data recovery apparatus based on local and global constraints according to claim 2, wherein the data processing module comprises an extraction unit, a low rank constraint unit, a noise extraction unit and a computation unit;
the extraction unit is used for extracting feature data of the multimedia data, and the feature data comprises global features and local features;
the low-rank constraint unit is used for performing low-rank constraint on the feature data by using the constructed data global constraint model;
the data global constraint model is as follows:
Figure FDA0003282509240000021
wherein the content of the first and second substances,
Figure FDA0003282509240000022
representing a clean data matrix, E representing a noisy data matrix, | A | | luminancew,*Represents a weighted nuclear norm, Tr (C (wA) DT) Representing matrices C (wA) DTThe trace of (a) is determined,
Figure FDA0003282509240000023
representing the characteristic data, C and D are unitary matrixes obtained by performing singular value decomposition on A,
Figure FDA0003282509240000024
the expression that the singular values of the data a are truncated and weighted is low-rank constrained, namely: firstly, removing the first r larger eigenvalues, then endowing different weights to the rest singular values according to different importance of the singular values, wherein I represents an identity matrix, and r represents that the first r larger singular values are removed after the singular values are sequenced from large to small;
the noise extraction unit is used for extracting noise in the feature data by using the constructed noise data local similarity measurement model;
the noise data local similarity measurement model is as follows:
Figure FDA0003282509240000025
wherein, gσ(. is a Gaussian kernel function, and
Figure FDA0003282509240000026
σ denotes the nuclear size, EiRow i representing the noisy data matrix E;
and the computing unit is used for solving the formula (1) and the formula (2) iteratively by using an alternating minimization method or an alternating direction multiplier method so as to obtain clean data and noise data.
CN202110925533.0A 2021-08-12 2021-08-12 Multimedia data self-adaptive recovery method and device based on local and global constraints Active CN113378415B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110925533.0A CN113378415B (en) 2021-08-12 2021-08-12 Multimedia data self-adaptive recovery method and device based on local and global constraints

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110925533.0A CN113378415B (en) 2021-08-12 2021-08-12 Multimedia data self-adaptive recovery method and device based on local and global constraints

Publications (2)

Publication Number Publication Date
CN113378415A CN113378415A (en) 2021-09-10
CN113378415B true CN113378415B (en) 2021-11-02

Family

ID=77577011

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110925533.0A Active CN113378415B (en) 2021-08-12 2021-08-12 Multimedia data self-adaptive recovery method and device based on local and global constraints

Country Status (1)

Country Link
CN (1) CN113378415B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102509268A (en) * 2011-11-09 2012-06-20 北方民族大学 Immune-clonal-selection-based nonsubsampled contourlet domain image denoising method
CN105931264A (en) * 2016-04-14 2016-09-07 西安电子科技大学 Sea-surface infrared small object detection method
CN106504207A (en) * 2016-10-24 2017-03-15 西南科技大学 A kind of image processing method
CN108765330A (en) * 2018-05-21 2018-11-06 西北大学 Image de-noising method and device based on the joint constraint of global and local priori
CN109754018A (en) * 2019-01-09 2019-05-14 北京工业大学 A kind of image-recognizing method of the low-rank locality preserving projections based on F norm
CN110992292A (en) * 2019-12-09 2020-04-10 河北工业大学 Enhanced low-rank sparse decomposition model medical CT image denoising method
CN111951181A (en) * 2020-07-14 2020-11-17 浙江工业大学 Hyperspectral image denoising method based on non-local similarity and weighted truncation kernel norm
CN111951186A (en) * 2020-07-16 2020-11-17 南京邮电大学 Hyperspectral image denoising method based on low-rank and total variation constraint

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103049892B (en) * 2013-01-27 2015-05-27 西安电子科技大学 Non-local image denoising method based on similar block matrix rank minimization
CN105872315B (en) * 2016-04-01 2019-03-08 广西大学 A kind of video denoising method for mixed noise

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102509268A (en) * 2011-11-09 2012-06-20 北方民族大学 Immune-clonal-selection-based nonsubsampled contourlet domain image denoising method
CN105931264A (en) * 2016-04-14 2016-09-07 西安电子科技大学 Sea-surface infrared small object detection method
CN106504207A (en) * 2016-10-24 2017-03-15 西南科技大学 A kind of image processing method
CN108765330A (en) * 2018-05-21 2018-11-06 西北大学 Image de-noising method and device based on the joint constraint of global and local priori
CN109754018A (en) * 2019-01-09 2019-05-14 北京工业大学 A kind of image-recognizing method of the low-rank locality preserving projections based on F norm
CN110992292A (en) * 2019-12-09 2020-04-10 河北工业大学 Enhanced low-rank sparse decomposition model medical CT image denoising method
CN111951181A (en) * 2020-07-14 2020-11-17 浙江工业大学 Hyperspectral image denoising method based on non-local similarity and weighted truncation kernel norm
CN111951186A (en) * 2020-07-16 2020-11-17 南京邮电大学 Hyperspectral image denoising method based on low-rank and total variation constraint

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Method to improve convergence performance of iterative learning control systems with bounded noise;Li, Li等;《JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS》;20200229;第357卷(第3期);1644-1670 *
基于块协同法向滤波的网格保特征去噪算法;黄槿;《中国优秀硕士学位论文全文数据库 (信息科技辑)》;20200215(第2期);I138-1715 *

Also Published As

Publication number Publication date
CN113378415A (en) 2021-09-10

Similar Documents

Publication Publication Date Title
Ullah et al. Light-DehazeNet: a novel lightweight CNN architecture for single image dehazing
CN111028177A (en) Edge-based deep learning image motion blur removing method
CN109446877B (en) Radar radiation source signal modulation identification method combined with multi-dimensional feature migration fusion
Liu et al. Knowledge-driven deep unrolling for robust image layer separation
CN107274365A (en) A kind of mine image intensification method based on unsharp masking and NSCT algorithms
CN114936979B (en) Model training method, image denoising method, device, equipment and storage medium
WO2023082453A1 (en) Image processing method and device
CN114255337A (en) Method and device for correcting document image, electronic equipment and storage medium
CN103632372B (en) A kind of video saliency image extraction method
CN113378415B (en) Multimedia data self-adaptive recovery method and device based on local and global constraints
CN113658065A (en) Image noise reduction method and device, computer readable medium and electronic equipment
CN109410134A (en) A kind of self-adaptive solution method based on image block classification
Li Dangerous posture monitoring for undersea diver based on frame difference method
CN110555406B (en) Video moving target identification method based on Haar-like characteristics and CNN matching
CN109886996B (en) Visual tracking optimization method
CN116612032A (en) Sonar image denoising method and device based on self-adaptive wiener filtering and 2D-VMD
Hou et al. Relative gradients for image lighting correction
Wang et al. Feature representation and compression methods for event-based data
Li et al. Real-time action recognition by feature-level fusion of depth and inertial sensor
Jian et al. Towards reliable object representation via sparse directional patches and spatial center cues
CN113256528B (en) Low-illumination video enhancement method based on multi-scale cascade depth residual error network
CN112767261A (en) Non-local denoising framework for color images and videos based on generalized non-convex tensor robust principal component analysis model
CN113903071A (en) Face recognition method and device, electronic equipment and storage medium
CN112883916B (en) Video denoising method based on image block similarity prior
CN113378917A (en) Event camera target identification method based on self-attention mechanism

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant