WO2019011017A1 - 噪点处理方法及装置 - Google Patents

噪点处理方法及装置 Download PDF

Info

Publication number
WO2019011017A1
WO2019011017A1 PCT/CN2018/082223 CN2018082223W WO2019011017A1 WO 2019011017 A1 WO2019011017 A1 WO 2019011017A1 CN 2018082223 W CN2018082223 W CN 2018082223W WO 2019011017 A1 WO2019011017 A1 WO 2019011017A1
Authority
WO
WIPO (PCT)
Prior art keywords
pixel
dispersion
pixel points
area array
depth information
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.)
Ceased
Application number
PCT/CN2018/082223
Other languages
English (en)
French (fr)
Inventor
简羽鹏
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.)
Autel Robotics Co Ltd
Original Assignee
Autel Robotics Co Ltd
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 Autel Robotics Co Ltd filed Critical Autel Robotics Co Ltd
Publication of WO2019011017A1 publication Critical patent/WO2019011017A1/zh
Priority to US16/740,233 priority Critical patent/US11238563B2/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/55—Depth or shape recovery from multiple images
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20076—Probabilistic image processing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20224—Image subtraction

Definitions

  • the present invention relates to the field of optics, and in particular, to a noise processing method and apparatus.
  • TOF is the abbreviation of Time of Flight technology, that is, the sensor shoots an object shot by a 3D camera that measures depth of field, emits modulated near-infrared light, and receives near-infrared light that is reflected after the object is encountered, thereby the sensor is calculated.
  • the time difference or phase difference between light emission and reflection to convert the distance of the object to be produced to generate depth information.
  • the three-dimensional contour of the object is presented in different colors to represent different depth information.
  • a part of noise is generated in the received light after reflection due to an external cause such as a component near the wavelength of the modulated light or a bright or dark irregularity of the reflecting surface. This part of the noise will interfere with the accuracy of the depth information.
  • the present invention provides a noise processing method and apparatus.
  • a noise processing method comprising:
  • the pixel points are identified as noise and rejected by the area array when at least one dispersion of the pixel points exceeds a predetermined threshold.
  • a noise processing device comprising:
  • a selection module configured to select pixel points in the area array, wherein the area array is formed by matrix arrangement of a plurality of the pixel points;
  • An obtaining module configured to acquire pre-stored depth information corresponding to the pixel point
  • An operation module configured to perform a pixel point dispersion operation according to the depth information corresponding to the pixel point, to obtain at least one dispersion of the pixel point, where the dispersion is used to indicate the pixel point and the rest of the area array The degree of dispersion between pixels;
  • the culling module is configured to identify the pixel points as noise and to be rejected by the area array when at least one dispersion of the pixel points exceeds a preset threshold.
  • a noise processing device comprising:
  • a memory for storing processor executable instructions
  • processor is configured to:
  • the pixel points are identified as noise and rejected by the area array when at least one dispersion of the pixel points exceeds a predetermined threshold.
  • the area array is formed by matrix arrangement of a plurality of pixel points; acquiring depth information corresponding to the pre-stored pixel points; performing pixel point dispersion calculation according to depth information corresponding to the pixel points, to obtain at least pixel points a degree of dispersion, which is used to indicate the degree of dispersion between the pixel points and the remaining pixels in the area array; when at least one dispersion of the pixel points exceeds a preset threshold, the pixel points are identified as noise and are eliminated by the area array .
  • the pixel points in the area array are selected, and the dispersion degree of the pixel points and the remaining pixels in the area array is calculated according to the depth information corresponding to the pixel points, and when at least one dispersion of the pixel points exceeds a preset threshold,
  • the pixel points are recognized as noise and are eliminated by the area array, thereby realizing the recognition and elimination of noise in the area array, thereby solving the technical problem that the generated noise affects the accuracy of the interference depth information.
  • FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment
  • FIG. 2 is a flowchart of a noise processing method according to an exemplary embodiment
  • FIG. 3 is a flow chart of performing pixel point dispersion calculation according to depth information corresponding to a pixel point in the corresponding embodiment of FIG. 2 to obtain at least one dispersion of pixel points in one embodiment;
  • FIG. 4 is a schematic diagram of a sample set, according to an exemplary embodiment
  • FIG. 5 is a flowchart of a noise processing method according to another exemplary embodiment
  • FIG. 6 is a flow chart of performing pixel point dispersion calculation according to depth information corresponding to a pixel point in accordance with the embodiment of FIG. 2 to obtain at least one dispersion of pixel points in another embodiment;
  • FIG. 7 is a schematic diagram showing adjacent pixel points according to an exemplary embodiment
  • FIG. 8 is a flowchart of a noise processing method according to another exemplary embodiment
  • FIG. 9 is a block diagram of a noise processing apparatus according to an exemplary embodiment.
  • FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. It should be noted that the electronic device 100 is merely an example that is adapted to the present invention and is not considered to provide any limitation on the scope of use of the present invention. The electronic device 100 is also not to be construed as having to rely on or must have one or more of the exemplary electronic devices 100 illustrated in FIG.
  • the electronic device 100 includes a memory 101 , a memory controller 103 , one or more (only one shown) processor 105 , a peripheral interface 107 , a radio frequency module 109 , a positioning module 111 , and a camera module 113 .
  • the memory 101 can be used to store software programs and modules, such as the noise processing method and the program instructions and modules corresponding to the apparatus in the exemplary embodiment of the present invention, and the processor 105 executes the program instructions stored in the memory 101 to execute each The function and data processing, that is, the noise processing method implemented in the application running in the electronic device 100 described above.
  • the memory 101 serves as a carrier for resource storage, and may be a random storage medium such as a high speed random access memory, a nonvolatile memory such as one or more magnetic storage devices, flash memory, or other solid state memory.
  • the storage method can be short-term storage or permanent storage.
  • the peripheral interface 107 can include at least one wired or wireless network interface, at least one serial-to-parallel conversion interface, at least one input-output interface, and at least one USB interface, etc., for coupling external external input/output devices to the memory 101 and processing
  • the device 105 is configured to communicate with various external input/output devices.
  • the radio frequency module 109 is configured to transmit and receive electromagnetic waves, and realize mutual conversion between electromagnetic waves and electric signals, thereby communicating with other devices through a communication network.
  • the communication network includes a cellular telephone network, a wireless local area network, or a metropolitan area network, and the above communication networks can use various communication standards, protocols, and technologies.
  • the positioning module 111 is configured to acquire a geographic location where the electronic device 100 is currently located.
  • Examples of positioning module 111 include, but are not limited to, Global Positioning System (GPS), wireless local area network or mobile communication network based positioning technology.
  • the camera module 113 is used to take a picture or video.
  • the captured picture or video can be stored in the memory 101, and can also be sent to the upper computer through the radio frequency module 109.
  • the audio module 115 provides an audio interface to the user, which may include one or more microphone interfaces, one or more speaker interfaces, and one or more earphone interfaces. Audio data interaction with other devices through the audio interface.
  • the audio data can be stored in the memory 101 and can also be transmitted through the radio frequency module 109.
  • the touch screen 117 provides an input and output interface between the electronic device 100 and the user. Specifically, the user can perform an input operation, such as a click, a touch, a slide, or the like, through the touch screen 117 to cause the electronic device to respond to the input operation.
  • the electronic device 100 displays the output content formed by any one or combination of text, picture or video to the user through the touch screen 117.
  • the button module 119 includes at least one button for providing an interface for the user to input to the electronic device 100, and the user can cause the electronic device 100 to perform different functions by pressing different buttons.
  • the sound adjustment button can be used by the user to adjust the volume of the sound played by the electronic device 100.
  • FIG. 1 is merely illustrative, and the electronic device 100 may further include more or less components than those shown in FIG. 1, or have components different from those shown in FIG.
  • the components shown in Figure 1 can be implemented in hardware, software, or a combination thereof.
  • FIG. 2 is a flowchart of a noise processing method according to an exemplary embodiment. As shown in FIG. 2, the noise processing method can be performed by the electronic device 100 in FIG. 1, and can include the following steps.
  • step 210 the pixels in the area array are selected.
  • the area array is formed by matrix arrangement of a plurality of pixel points.
  • the array of matrices are arranged.
  • the pixels in the selected area array may be selected in the order of the array, or may be randomly sampled.
  • step 230 depth information corresponding to the pixel points stored in advance is acquired.
  • the sensor receives the reflected light and calculates the phase difference between the pixel in the light and the light emitted by the sensor.
  • the depth information may be a phase difference, or may be a depth distance value calculated according to the phase difference, the depth distance value indicating a distance between the sensor and an object that reflects the light.
  • the depth information is stored in the sensor, so that the degree of dispersion of the pixel from other pixels can be calculated according to the depth information, thereby eliminating the pixels with excessively large discrete points. It will be appreciated that in other embodiments, the depth information may also be stored in a memory other than the sensor.
  • step 250 the pixel point dispersion operation is performed according to the depth information corresponding to the pixel point, and at least one dispersion degree of the pixel point is obtained.
  • the dispersion is used to indicate the degree of dispersion between the pixel points and the remaining pixels in the area array.
  • the pixel point dispersion operation is a process of calculating the dispersion degree of the pixel points based on the depth information corresponding to the obtained pixel points and calculating the depth information of the other pixel points.
  • the dispersion is used to determine whether the pixel is a noise.
  • the pixel is determined.
  • the depth information corresponding to the point is not reliable, that is, the pixel is noise.
  • a plurality of dispersions corresponding to the pixel points may be generated, thereby obtaining one of the dispersion degrees for determining the dispersion degree of the pixel points, or may be based on multiple dispersion degrees. The determination of the pixel dispersion is performed.
  • step 270 the pixel points are identified as noise and rejected by the area array when at least one dispersion of the pixel points exceeds a predetermined threshold.
  • the preset threshold is a threshold for determining whether the pixel is a noise. When the dispersion exceeds the preset threshold, it is determined that the pixel corresponding to the dispersion is noise, and the pixel is culled, thereby realizing the recognition and rejection of the noise, and solving the noise that affects the accuracy of the interference depth information.
  • This embodiment solves the technical problem that the generated noise affects the accuracy of the interference depth information by realizing the recognition and elimination of noise in the area array.
  • the pixel points in the selected area array of the corresponding embodiment of FIG. 2 include in one step of an embodiment:
  • the pixel points are selected one by one according to the order of the pixels in the area array.
  • the pixel points in the area array are arranged according to a matrix. Therefore, the pixel points according to the arrangement order of the pixel points are selected one by one, and the selection manner may be selected according to the line matrix row or row, or may be performed according to one column in the area array. Select to avoid missing or reselecting pixels.
  • the pixel points are selected one by one according to the arrangement order of the pixel points in the area array, thereby avoiding missing or reselecting the pixel points to cause errors in the noise processing result.
  • the depth information corresponding to the acquiring pixel points of the corresponding embodiment of FIG. 2 includes the following steps in one embodiment:
  • phase difference of each pixel in the area array is obtained, and the phase difference of each pixel point is used as the depth information corresponding to the pixel point.
  • the sensor receives the reflected light and calculates the phase difference of the pixel points in the light, and the sensor acquires the phase difference between each pixel in the array and the light emitted by the sensor as the depth information corresponding to the pixel, thereby Information identifies and rejects noise.
  • This embodiment realizes acquiring the phase difference of each pixel in the area array, and using the phase difference as the depth information corresponding to the pixel point.
  • FIG. 3 is a depiction of the details of step 250, according to an exemplary embodiment. As shown in FIG. 3, the step 250 can include the following steps.
  • step 251 the selected pixel points are added to the sample set.
  • the sample set can exist in the form of a linked list.
  • the pointer is pointed to the pixel to be added, thereby adding the pixel to the sample set.
  • the pointer pointing to the pixel to be culled is pointed to the next pixel, thereby puncturing the pixel from the sample set.
  • the sample set is set with sample size, and the number of pixels in the sample set does not exceed the sample size.
  • the pixels are selected to be added to the sample set according to the order in which they are arranged in the area array.
  • the pixel that first enters the sample set is moved out of the sample set, thereby ensuring that the number of pixels does not exceed the sample size.
  • FIG. 4 is a schematic diagram of a sample set, according to an exemplary embodiment.
  • the sample size of the sample set is set to K, and the selected pixel is added to the sample set, and when the number of pixels of the sample set is greater than K, the pixel that first enters the sample set is moved out of the sample set.
  • step 252 the standard deviation of the pixel points is calculated according to the standard deviation formula with all the sample pixel points in the sample set as the input of the standard deviation formula.
  • K is the number of pixels in the sample set
  • x i is the depth information of the pixel. The average of the depth information for the pixels in the sample set.
  • step 253 the standard deviation of the pixel points is taken as the dispersion of the pixel points.
  • K takes a value of 50. It can be understood that the number of pixel points K in the sample set in the actual application may be other values, which are not strictly limited herein.
  • FIG. 5 is a flowchart of a noise processing method according to another exemplary embodiment. As shown in FIG. 5, before at least one dispersion of the pixel points exceeds a preset threshold, the pixel processing method may include the following steps before the pixel point is recognized as noise and is removed from the area array.
  • step 310 the standard deviation and the demarcation standard deviation are compared.
  • the demarcation standard deviation is a set preset threshold, and the general setting of the demarcation standard deviation is 0.5*maximum phase difference/maximum distance.
  • step 330 If the standard deviation exceeds the preset threshold, then the process proceeds to step 330.
  • the jump entry step selects the pixel points one by one according to the order of the pixels in the area array.
  • step 330 if the standard deviation exceeds the demarcation standard deviation, it is determined that at least one dispersion of the pixel points exceeds a preset threshold.
  • FIG. 6 is a depiction of the details of step 250, according to an exemplary embodiment. As shown in FIG. 6, the step 250 may further include the following steps.
  • step 256 a plurality of adjacent pixel points adjacent to the pixel point are obtained from the area array in a specified direction centering on the pixel point.
  • FIG. 7 is a schematic diagram of adjacent pixel points, according to an exemplary embodiment.
  • the specified direction may be four directions of southeast, northwest, and northeast, northeast, southwest, northwest, or eight directions.
  • the adjacent pixels are adjacent to the coordinates of the pixel in the area array.
  • the central pixel is (X 1 , Y 1 ), and (X 1 , Y 2 ) is the adjacent pixel in the south direction of the central pixel.
  • (X 2 , Y 2 ) is the adjacent pixel point in the southeast direction of the central pixel point.
  • a pixel on the boundary there will be no adjacent pixels in one of the pixels at this time.
  • the adjacent pixel point acquisition in the specified direction is not performed.
  • step 257 depth information corresponding to each adjacent pixel point stored in advance is acquired.
  • the depth information is pre-stored in the sensor. Thereby, the depth information corresponding to each adjacent pixel point can be acquired by the sensor.
  • the depth information corresponding to each adjacent pixel point is acquired, that is, the phase difference of the adjacent pixel points is acquired, so that the dispersion operation is performed according to the depth information.
  • step 258 depth information difference calculation between the pixel point and the adjacent pixel point is performed for the depth information corresponding to the pixel point and each adjacent pixel point.
  • ⁇ is the difference between the calculated depth information of the pixel point and the adjacent pixel point
  • Me is the depth information corresponding to the pixel point
  • Neighbor is the depth information corresponding to the adjacent pixel point.
  • the depth information difference between the pixel point and each adjacent pixel point is calculated by performing difference between the depth information corresponding to the pixel point and the depth information corresponding to each adjacent pixel point and taking an absolute value.
  • step 259 the calculated plurality of depth information differences are used as a plurality of dispersions of the pixel points.
  • This embodiment implements obtaining a plurality of dispersions of pixel points according to depth information corresponding to pixel points and adjacent pixel points.
  • FIG. 8 is a flowchart of a noise processing method according to another exemplary embodiment.
  • the noise processing method may include the following steps before the pixel point is recognized as noise and the step is eliminated from the area array when at least one dispersion of the pixel points exceeds a preset threshold.
  • step 410 a plurality of depth information differences and depth limits are compared, respectively.
  • the depth limit is a set preset threshold. Comparing the plurality of depth information difference values and the depth limit values respectively, thereby obtaining a plurality of comparison results, and determining, according to the plurality of comparison results, whether the at least one dispersion of the pixel points exceeds a preset threshold.
  • step 430 If at least one dispersion of the pixel points exceeds a predetermined threshold, the jump proceeds to step 430.
  • the returning step performs pixel selection one by one according to the order of the pixels in the area array.
  • step 430 if any of the depth information differences exceeds the depth limit, it is determined that at least one dispersion of the pixel points exceeds a preset threshold.
  • the pixel point has at least one dispersion exceeding a preset threshold, thereby determining that the pixel is noise and culling the pixel.
  • the pixel point is noise by determining that two or more dispersions exceed a preset threshold, thereby improving the accuracy of the noise cancellation.
  • This embodiment achieves determining whether the pixel point has at least one dispersion exceeding a preset threshold.
  • FIG. 9 is a block diagram of a noise processing apparatus according to an exemplary embodiment.
  • the apparatus performs all or part of the steps as described above.
  • the apparatus includes, but is not limited to, a selection module 510, an acquisition module 530, an operation module 550, and a culling module 570.
  • the selection module 510 is configured to select pixel points in the area array, and the area array is formed by matrix arrangement of a plurality of pixel points.
  • the obtaining module 530 is configured to obtain depth information corresponding to the pre-stored pixel points.
  • the operation module 550 is configured to perform a pixel point dispersion operation according to the depth information corresponding to the pixel point to obtain at least one dispersion degree of the pixel point, where the dispersion degree is used to indicate the degree of dispersion between the pixel point and the remaining pixel points in the area array.
  • the culling module 570 is configured to identify the pixel as noise and reject it from the area array when at least one dispersion of the pixel exceeds a preset threshold.
  • the selection module of the corresponding embodiment of FIG. 9 includes but is not limited to:
  • the sequence selection unit is configured to perform pixel selection one by one according to the arrangement order of the pixel points in the area array.
  • the invention includes a noise processing device.
  • the noise processing device performs all or part of the steps of the noise processing method shown in any of Fig. 2.
  • the noise processing device includes:
  • a memory for storing processor executable instructions.
  • processor is configured to:
  • the pixel points in the area array are selected, and the area array is formed by matrix arrangement of a plurality of pixel points.
  • the depth information corresponding to the pre-stored pixel points is obtained.
  • the pixel point dispersion operation is performed according to the depth information corresponding to the pixel point to obtain at least one dispersion degree of the pixel point, and the dispersion degree is used to indicate the degree of dispersion between the pixel point and the remaining pixel points in the area array.
  • the pixel When at least one degree of dispersion of the pixel exceeds a predetermined threshold, the pixel is identified as noise and rejected by the area array.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Evolutionary Biology (AREA)
  • Operations Research (AREA)
  • Probability & Statistics with Applications (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Algebra (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)

Abstract

本发明揭示了一种噪点处理方法及装置。所述方法包括：选取面阵中的像素点，面阵为多个像素点通过矩阵排列形成；获取预先存储的像素点对应的深度信息；根据像素点对应的深度信息进行像素点离散度运算，得到像素点的至少一离散度，离散度用于指示像素点与面阵中其余像素点之间的离散程度；在像素点的至少一离散度超过预设阈值时，将像素点识别为噪点并由面阵中剔除。由上述方法可知，根据像素点对应的深度信息计算像素点与面阵中其余像素点的离散度，并在像素点的至少一离散度超过预设阈值时，将像素点识别为噪点并由面阵中剔除，从而实现了识别并剔除面阵中的噪点，从而解决了产生的噪点会影响干扰深度信息的准确性的技术问题。

Description

噪点处理方法及装置
申请要求于2017年7月11日申请的、申请号为201710562270.5、申请名称为“噪点处理方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及光学领域，特别涉及一种噪点处理方法及装置。
背景技术
TOF是飞行时间(Time of Flight)技术的缩写，即传感器向测量景深的3D相机拍摄的物体，发出经调制的近红外光，并接收遇物体后反射的近红外光，由此，传感器通过计算光线发射和反射的时间差或相位差，来换算被拍摄物体的距离，以产生深度信息。再结合传统的相机拍摄，将物体的三维轮廓以不同颜色代表不同深度信息的方式呈现出来。
在该TOF技术中，由于外界光源中存在接近调制光波长的成分或反射面的亮暗不规则等外部原因，反射后接收到的光线中会产生一部分噪点。这部分噪点会干扰深度信息的准确性。
发明内容
为了解决相关技术中存在的反射后接收到的光线中产生的噪点会干扰深度信息的准确性的技术问题，本发明提供了一种噪点处理方法及装置。
一种噪点处理方法，所述方法包括：
选取面阵中的像素点，所述面阵为多个所述像素点通过矩阵排列形成；
获取预先存储的所述像素点对应的深度信息；
根据所述像素点对应的深度信息进行像素点离散度运算，得到所述像素点的至少一离散度，所述离散度用于指示所述像素点与所述面阵中其余像素点之间的离散程度；
在所述像素点的至少一离散度超过预设阈值时，将所述像素点识别为噪点并由所述面阵中剔除。
一种噪点处理装置，所述装置包括：
选取模块，用于选取面阵中的像素点，所述面阵为多个所述像素点通过矩阵排列形成；
获取模块，用于获取预先存储的所述像素点对应的深度信息；
运算模块，用于根据所述像素点对应的深度信息进行像素点离散度运算，得到所述像素点的至少一离散度，所述离散度用于指示所述像素点与所述面阵中其余像素点之间的离散程度；
剔除模块，用于在所述像素点的至少一离散度超过预设阈值时，将所述像素点识别为噪点并由所述面阵中剔除。
一种噪点处理装置，所述装置包括：
处理器；
用于存储处理器可执行指令的存储器；
其中，所述处理器被配置为：
选取面阵中的像素点，所述面阵为多个所述像素点通过矩阵排列形成；
获取预先存储的所述像素点对应的深度信息；
根据所述像素点对应的深度信息进行像素点离散度运算，得到所述像素点的至少一离散度，所述离散度用于指示所述像素点与所述面阵中其余像素点之间的离散程度；
在所述像素点的至少一离散度超过预设阈值时，将所述像素点识别为噪点并由所述面阵中剔除。
本发明的实施例提供的技术方案可以包括以下有益效果：
选取面阵中的像素点，面阵为多个像素点通过矩阵排列形成；获取预先存储的像素点对应的深度信息；根据像素点对应的深度信息进行像素点离散度运算，得到像素点的至少一离散度，离散度用于指示像素点与面阵中其余像素点之间的离散程度；在像素点的至少一离散度超过预设阈值时，将像素点识别为 噪点并由面阵中剔除。由上述方法可知，选取面阵中的像素点，并根据像素点对应的深度信息计算像素点与面阵中其余像素点的离散度，并在像素点的至少一离散度超过预设阈值时，将像素点识别为噪点并由面阵中剔除，从而实现了识别并剔除面阵中的噪点，从而解决了产生的噪点会影响干扰深度信息的准确性的技术问题。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性的，并不能限制本发明。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本发明的实施例，并于说明书一起用于解释本发明的原理。显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他实施例的附图。
图1是根据一示例性实施例示出的一种电子设备的框图；
图2是根据一示例性实施例示出的一种噪点处理方法的流程图；
图3是图2对应实施例的根据像素点对应的深度信息进行像素点离散度运算，得到像素点的至少一离散度在一个实施例的流程图；
图4是根据一示例性实施例示出的样本集合的示意图；
图5是根据另一示例性实施例示出的一种噪点处理方法的流程图；
图6是图2对应实施例的根据像素点对应的深度信息进行像素点离散度运算，得到像素点的至少一离散度在另一个实施例的流程图；
图7是根据一示例性实施例示出的相邻像素点的示意图；
图8是根据另一示例性实施例示出的一种噪点处理方法的流程图；
图9是根据一示例性实施例示出的一种噪点处理装置框图。
具体实施方式
这里将详细地对示例性实施例执行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本发明相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的、本发明的一些方面相一 致的装置和方法的例子。
请参阅图1，图1是根据一示例性实施例示出的一种电子设备的框图。需要说明的是，该电子设备100只是一个适配于本发明的示例，不能认为是提供了对本发明的使用范围的任何限制。该电子设备100也不能解释为需要依赖于或者必须具有图1中示出的示例性的电子设备100中的一个或者多个组件。
如图1所示，电子设备100包括存储器101、存储控制器103、一个或多个(图中仅示出一个)处理器105、外设接口107、射频模块109、定位模块111、摄像模块113、音频模块115、触控屏幕117以及按键模块119。这些组件通过一条或多条通讯总线/信号线121相互通讯。
其中，存储器101可用于存储软件程序以及模块，如本发明示例性实施例中的噪点处理方法及装置对应的程序指令及模块，处理器105通过运行存储在存储器101内的程序指令，从而执行各种功能以及数据处理，即实现上述运行于电子设备100的应用程序中的噪点处理方法。
存储器101作为资源存储的载体，可以是随机存储介质、例如高速随机存储器、非易失性存储器，如一个或多个磁性存储装置、闪存、或者其它固态存储器。存储方式可以是短暂存储或者永久存储。
外设接口107可以包括至少一有线或无线网络接口、至少一串并联转换接口、至少一输入输出接口以及至少一USB接口等等，用于将外部各种输入/输出装置耦合至存储器101以及处理器105，以实现与外部各种输入/输出装置的通信。
射频模块109用于收发电磁波，实现电磁波与电信号的相互转换，从而通过通讯网络与其他设备进行通讯。通信网络包括蜂窝式电话网、无线局域网或者城域网，上述通信网络可以使用各种通信标准、协议及技术。
定位模块111用于获取电子设备100的当前所在的地理位置。定位模块111的实例包括但不限于全球卫星定位系统(GPS)、基于无线局域网或者移动通信网的定位技术。
摄像模块113用于拍摄图片或者视频。拍摄的图片或者视频可以存储至存储器101内，还可以通过射频模块109发送至上位机。
音频模块115向用户提供音频接口，其可包括一个或多个麦克风接口、一 个或多个扬声器接口以及一个或多个耳机接口。通过音频接口与其它设备进行音频数据的交互。音频数据可以存储至存储器101内，还可以通过射频模块109发送。
触控屏幕117在电子设备100与用户之间提供一个输入输出界面。具体地，用户可通过触控屏幕117进行输入操作，例如点击、触摸、滑动等手势操作，以使电子设备对该输入操作进行响应。电子设备100则将文字、图片或者视频任意一种形式或者组合所形成的输出内容通过触控屏幕117向用户显示输出。
按键模块119包括至少一个按键，用以提供用户向电子设备100进行输入的接口，用户可以通过按下不同的按键使电子设备100执行不同的功能。例如，声音调节按键可供用户实现对电子设备100播放的声音音量的调节。
可以理解，图1所示的结构仅为示意，电子设备100还可包括比图1中所示更多或更少的组件，或者具有与图1所示不同的组件。图1中所示的各组件可以采用硬件、软件或者其组合来实现。
图2是根据一示例性实施例示出的一种噪点处理方法的流程图。如图2所示，该噪点处理方法可以由图1中的电子设备100执行，可以包括以下步骤。
在步骤210中，选取面阵中的像素点。
其中，面阵为多个像素点通过矩阵排列形成。传感器接收到的反射后的光线是由多个像素点组成的，例如接收到的光线分辨率为320*240像素，则光线中含有320*240＝76800个像素点，这些像素点即构成一个排列成矩阵的面阵。
进一步地，选取面阵中的像素点可以按照面阵中的顺序进行选取，也可以是随机抽样进行选取。
在步骤230中，获取预先存储的像素点对应的深度信息。
其中，传感器接收到反射后的光线，并计算光线中像素点与传感器发射的光线的相位差。
基于此，在一个示例性实施例中，深度信息可以为相位差，也可以为根据相位差计算得到的深度距离值，该深度距离值表示传感器与反射光线的物体的距离。
其中，计算深度距离值的公式为：深度距离值＝相位差*最大距离/最大相位差，其中，最大距离与传感器发出光线调制的频率相关，最大相位差为传感器 芯片相关的参数，二者均为固定值。
获取到深度信息后，便将深度信息存储至传感器中，从而可以根据深度信息，计算该像素点与其他像素点的离散程度，从而将离散程度过大的像素点剔除。可以理解，在其他实施例中，深度信息也可以存储在传感器以外的存储器中。
在步骤250中，根据像素点对应的深度信息进行像素点离散度运算，得到像素点的至少一离散度。
其中，离散度用于指示像素点与面阵中其余像素点之间的离散程度。像素点离散度运算为根据得到的像素点对应的深度信息，与其他像素点的深度信息进行计算，从而得到像素点的离散度的过程。
离散度用于判断该像素点是否为噪点，当该像素点与其他像素点的离散度过大时，表示该像素点与面阵中其余像素点之间的离散程度过大，则判断该像素点对应的深度信息不可靠，即该像素点为噪点。
进一步地，离散度运算过程中，可能会产生多个对应于像素点的离散度，由此，可以获取其中一个离散度进行该像素点离散度的判断，也可以是以多个离散度为基础进行该像素点离散度的判断。
在步骤270中，在像素点的至少一离散度超过预设阈值时，将像素点识别为噪点并由所述面阵中剔除。
其中，预设阈值为判断像素点是否为噪点的临界值。当离散度超过预设阈值时，则判定该离散度对应的像素点为噪点，并将该像素点剔除，从而实现了噪点的识别和剔除，解决了产生的噪点会影响干扰深度信息的准确性的技术问题。
此实施例通过实现识别并剔除面阵中的噪点，从而解决了产生的噪点会影响干扰深度信息的准确性的技术问题。
在一个示例性实施例中，图2对应实施例的选取面阵中的像素点在一个实施例的步骤包括：
按照面阵中像素点的排列顺序进行像素点逐一选取。
其中，面阵中的像素点按照矩阵排列，由此，按照像素点的排列顺序进行的像素点逐一选取，选取方式可以按照面阵一行行的进行选取，也可以按照面 阵中一列列的进行选取，以此避免了漏选或者重选像素点。
在一个示例性实施例的具体实现中，面阵中光线中的分辨率设置为320*240像素，光线中含有320*240＝76800个像素点，可设置像素点的坐标为(X 0，Y 0)，(X 1，Y 0)…(X 319，Y 239)。按照像素点坐标的排列顺序，逐一选取像素点，保证面阵中所有的像素点都能进行噪点识别。
此实施例实现了按照面阵中像素点的排列顺序进行像素点逐一选取，从而避免了漏选或者重选像素点使噪点处理结果产生误差。
在一个示例性实施例中，图2对应实施例的获取像素点对应的深度信息在一个实施例的步骤包括：
获取面阵中各像素点的相位差，以各像素点的相位差作为像素点对应的深度信息。
其中，传感器接收到经反射后的光线并计算光线中的像素点的相位差，由传感器获取面阵中各像素点与传感器发射的光线的相位差，作为像素点对应的深度信息，从而根据深度信息识别并剔除噪点。
此实施例实现了获取面阵中各像素点的相位差，并将相位差作为像素点对应的深度信息。
图3是根据一示例性实施例示出的对步骤250的细节进行描述。如图3所示，该步骤250可以包括以下步骤。
在步骤251中，将选取到的像素点添加至样本集合。
其中，样本集合可以以链表的形式存在。当添加像素点时，将指针指向欲添加的像素点，从而将像素点添加至样本集合中。当剔除像素点时，将指向欲剔除的像素点的指针指向下一个像素点，从而将像素点剔除样本集合中。
样本集合设置有样本容量，样本集合中的像素点个数不超过样本容量。按照面阵中排列顺序选取像素点添加至样本集合。当样本集合中的像素点的个数超过样本容量时，将最先进入样本集合的像素点移出样本集合外，从而保证像素点个数不超过样本容量。
如图4所示，图4是根据一示例性实施例示出的样本集合的示意图。样本集合的样本容量设置为K，选取像素点添加至样本集合，并当样本集合的像素 点的个数大于K时，将最先进入样本集合的像素点移出样本集合外。
在步骤252中，以样本集合中的所有样本像素点作为标准差公式的输入，依照标准差公式计算得到像素点的标准差。
其中，计算标准差的公式为：
Figure PCTCN2018082223-appb-000001
其中，K为样本集合中的像素点个数，x i为像素点的深度信息，
Figure PCTCN2018082223-appb-000002
为样本集合中的像素点的深度信息的平均值。
在步骤253中，以像素点的标准差作为像素点的离散度。
在一个实施例中，K取值为50。可以理解，实际应用中样本集合中的像素点个数K可以为其他值，这里不作严格限定。
图5是根据另一示例性实施例示出的一种噪点处理方法的流程图。如图5所示，在像素点的至少一离散度超过预设阈值时，将像素点识别为噪点并由面阵中剔除步骤之前，该噪点处理方法可以包括以下步骤。
在步骤310中，比较标准差与分界标准差。
在一个实例性实施例的具体实现中，分界标准差为设置的预设阈值，分界标准差的一般设置为0.5*最大相位差/最大距离。
通过比较标准差与分界标准差判断标准差是否超过预设阈值。
如果标准差超过预设阈值，则跳转进入步骤330。
反之，如果标准差未超过预设阈值，则跳转进入步骤按照面阵中像素点的排列顺序进行像素点逐一选取。
在步骤330中，如果标准差超过分界标准差，则判定像素点的至少一离散度超过预设阈值。
图6是根据一示例性实施例示出的对步骤250的细节进行描述。如图6所示，该步骤250还可以包括以下步骤。
在步骤256中，以像素点为中心，按照指定方向由面阵中获得与像素点相邻的多个相邻像素点。
如图7所示，图7是根据一示例性实施例示出的相邻像素点的示意图。
在一个示例性实施例的具体实现中，指定方向可以为东南西北四个方向， 也可以包括东南，东北，西南，西北四个方向或者八个方向。
相邻像素点即为像素点在面阵中的坐标相邻，例如中心像素点为(X 1，Y 1)，(X 1，Y 2)即为中心像素点南面方向的相邻像素点，(X 2，Y 2)即为中心像素点东南方向的相邻像素点。
当选取的像素点为边界上的像素点，即在320*240的面阵中，X i＝0,或X i＝319或Y i＝0或Y i＝239时，即表示选取的像素点为边界上的像素点，此时的像素点会存在其中一个方向上没有相邻像素点，例如(X 0，Y 1)不存在西面方向上的相邻像素点
具体地，可以根据像素点的坐标，判定像素点不存在某一指定方向上的相邻像素点时，不进行这一指定方向上的相邻像素点获取。
在步骤257中，获取预先存储的各相邻像素点对应的深度信息。
如前所述，深度信息预先存储在传感器中。由此，便可由传感器获取到各相邻像素点对应的深度信息。
在一个示例性实施例的具体实现中，获取各相邻像素点对应的深度信息，即获取相邻像素点的相位差，从而根据深度信息进行离散度运算。
在步骤258中，针对像素点和各相邻像素点分别对应的深度信息，进行像素点与相邻像素点之间深度信息差值计算。
其中，深度信息差值计算公式为：
σ＝|Me-Neighbor|
其中，σ为计算得到的像素点与相邻像素点之间深度信息的差值，Me为像素点对应的深度信息，Neighbor为相邻像素点对应的深度信息。通过将像素点对应的深度信息和各个相邻像素点对应的深度信息分别进行作差并取绝对值，计算出像素点与各个相邻像素点之间的深度信息差值。
在步骤259中，以计算得到的多个深度信息差值作为像素点的多个离散度。
此实施例实现了根据像素点和相邻像素点对应的深度信息，实现获取像素点的多个离散度。
图8是根据另一示例性实施例示出的一种噪点处理方法的流程图。如图8所示，在像素点的至少一离散度超过预设阈值时，将像素点识别为噪点并由面阵中剔除步骤之前，该噪点处理方法可以包括以下步骤。
在步骤410中，分别比较多个深度信息差值与深度限值。
在一个实例性实施例的具体实现中，深度限值为设置的预设阈值。分别比较多个深度信息差值和深度限值，从而得到多个比较结果，并根据该多个比较结果判断像素点的至少一离散度是否超过预设阈值。
如果像素点的至少一离散度超过预设阈值，跳转进入步骤430。
反之，如果像素点的至少一离散度未超过预设阈值，则返回步骤按照面阵中像素点的排列顺序进行像素点逐一选取。
在步骤430中，如果任一个深度信息差值超过深度限值，则判定像素点的至少一离散度超过预设阈值。
其中，只要存在一个深度信息差值超过深度限值时，即判定像素点存在至少一离散度超过预设阈值，从而判断该像素点为噪点，并将该像素点剔除。
当然，在其他应用场景中，还可以通过判断两个以上的离散度超过预设阈值时，才判定像素点为噪点，以此来提高剔除噪点的精度。
此实施例实现了判断像素点是否有至少一离散度超过预设阈值。
图9是根据一示例性实施例示出的一种噪点处理装置框图。该装置执行如上所述的全部或者部分步骤。如图9所示，该装置包括但不限于：选取模块510，获取模块530，运算模块550和剔除模块570。
选取模块510用于选取面阵中的像素点，面阵为多个像素点通过矩阵排列形成。
获取模块530用于获取预先存储的像素点对应的深度信息。
运算模块550用于根据像素点对应的深度信息进行像素点离散度运算，得到像素点的至少一离散度，离散度用于指示像素点与面阵中其余像素点之间的离散程度。
剔除模块570用于在像素点的至少一离散度超过预设阈值时，将像素点识别为噪点并由面阵中剔除。
在一个示例性实施例中，图9对应实施例的选取模块包括但不限于：
顺序选取单元，用于按照面阵中像素点的排列顺序进行像素点逐一选取。
在一个示例性实施例中，本发明包括一种噪点处理装置。该噪点处理装置 执行图2任一所示的噪点处理方法的全部或者部分步骤。该噪点处理装置包括：
处理器。
用于存储处理器可执行指令的存储器。
其中，处理器被配置为：
选取面阵中的像素点，面阵为多个像素点通过矩阵排列形成。
获取预先存储的像素点对应的深度信息。
根据像素点对应的深度信息进行像素点离散度运算，得到像素点的至少一离散度，离散度用于指示像素点与面阵中其余像素点之间的离散程度。
在像素点的至少一离散度超过预设阈值时，将像素点识别为噪点并由面阵中剔除。
上述装置中各个模块的功能和作用的实现过程详见上述噪点处理方法中对应步骤的实现过程，在此不再赘述。
应当理解的是，本发明并不局限于上面已经描述并在附图中示出的精确结构，并且可以在不脱离其范围执行各种修改和改变。本发明的范围仅由所附的权利要求来限制。

Claims (10)

  1. 一种噪点处理方法，其特征在于，所述方法包括：
    选取面阵中的像素点，所述面阵为多个所述像素点通过矩阵排列形成；
    获取预先存储的所述像素点对应的深度信息；
    根据所述像素点对应的深度信息进行像素点离散度运算，得到所述像素点的至少一离散度，所述离散度用于指示所述像素点与所述面阵中其余像素点之间的离散程度；
    在所述像素点的至少一离散度超过预设阈值时，将所述像素点识别为噪点并由所述面阵中剔除。
  2. 根据权利要求1所述的方法，其特征在于，所述选取面阵中的像素点步骤包括：
    按照所述面阵中像素点的排列顺序进行像素点逐一选取。
  3. 根据权利要求1所述的方法，其特征在于，所述获取预先存储的所述像素点对应的深度信息步骤包括：
    获取所述面阵中各像素点的相位差，以所述各像素点的相位差作为所述像素点对应的深度信息。
  4. 根据权利要求1至3任一项所述的方法，其特征在于，所述根据所述像素点对应的深度信息进行像素点离散度运算，得到所述像素点的至少一离散度步骤包括：
    将选取到的所述像素点添加至样本集合；
    以所述样本集合中的所有样本像素点作为标准差公式的输入，依照标准差公式计算得到所述像素点的标准差；
    以所述像素点的标准差作为所述像素点的离散度。
  5. 根据权利要求4所述的方法，其特征在于，所述预设阈值为分界标准差，所述在所述像素点的至少一离散度超过预设阈值时，将所述像素点识别为噪点 并由所述面阵中剔除步骤之前，所述方法还包括：
    比较所述标准差与所述分界标准差；
    如果所述标准差超过所述分界标准差，则判定所述像素点的至少一离散度超过所述预设阈值。
  6. 根据权利要求1至3任一项所述的方法，其特征在于，所述根据所述像素点对应的深度信息进行像素点离散度运算，得到所述像素点的至少一离散度步骤还包括：
    以所述像素点为中心，按照指定方向由所述面阵中获得与所述像素点相邻的多个相邻像素点；
    获取预先存储的各所述相邻像素点对应的深度信息；
    针对所述像素点和各所述相邻像素点分别对应的深度信息，进行像素点与相邻像素点之间深度信息差值计算；
    以计算得到的多个深度信息差值作为所述像素点的多个离散度。
  7. 根据权利要求6所述的方法，其特征在于，所述预设阈值为深度限值，所述在所述像素点的至少一离散度超过预设阈值时，将所述像素点识别为噪点并由所述面阵中剔除步骤之前，所述方法还包括：
    分别比较多个所述深度信息差值与所述深度限值；
    如果任一个所述深度信息差值超过所述深度限值，则判定所述像素点的至少一离散度超过所述预设阈值。
  8. 一种噪点处理装置，其特征在于，所述装置包括：
    选取模块，用于选取面阵中的像素点，所述面阵为多个所述像素点通过矩阵排列形成；
    获取模块，用于获取预先存储的所述像素点对应的深度信息；
    运算模块，用于根据所述像素点对应的深度信息进行像素点离散度运算，得到所述像素点的至少一离散度，所述离散度用于指示所述像素点与所述面阵中其余像素点之间的离散程度；
    剔除模块，用于在所述像素点的至少一离散度超过预设阈值时，将所述像 素点识别为噪点并由所述面阵中剔除。
  9. 根据权利要求8所述的装置，其特征在于，所述选取模块包括：
    顺序选取单元，用于按照所述面阵中像素点的排列顺序进行像素点逐一选取。
  10. 一种噪点处理装置，其特征在于，所述装置包括：
    处理器；
    用于存储处理器可执行指令的存储器；
    其中，所述处理器被配置为：
    选取面阵中的像素点，所述面阵为多个所述像素点通过矩阵排列形成；
    获取预先存储的所述像素点对应的深度信息；
    根据所述像素点对应的深度信息进行像素点离散度运算，得到所述像素点的至少一离散度，所述离散度用于指示所述像素点与所述面阵中其余像素点之间的离散程度；
    在所述像素点的至少一离散度超过预设阈值时，将所述像素点识别为噪点并由所述面阵中剔除。
PCT/CN2018/082223 2017-07-11 2018-04-08 噪点处理方法及装置 Ceased WO2019011017A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US16/740,233 US11238563B2 (en) 2017-07-11 2020-01-10 Noise processing method and apparatus

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201710562270.5A CN109242782B (zh) 2017-07-11 2017-07-11 噪点处理方法及装置
CN201710562270.5 2017-07-11

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US16/740,233 Continuation US11238563B2 (en) 2017-07-11 2020-01-10 Noise processing method and apparatus

Publications (1)

Publication Number Publication Date
WO2019011017A1 true WO2019011017A1 (zh) 2019-01-17

Family

ID=65000878

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/082223 Ceased WO2019011017A1 (zh) 2017-07-11 2018-04-08 噪点处理方法及装置

Country Status (3)

Country Link
US (1) US11238563B2 (zh)
CN (1) CN109242782B (zh)
WO (1) WO2019011017A1 (zh)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109242901B (zh) * 2017-07-11 2021-10-22 深圳市道通智能航空技术股份有限公司 应用于三维相机的图像校准方法和装置
CN111415308B (zh) * 2020-03-13 2023-04-28 青岛海信医疗设备股份有限公司 一种超声图像处理方法和通信终端
CN115294314B (zh) * 2022-09-26 2022-12-30 南通蘅田电子科技有限公司 一种电子元器件表面缺陷识别方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102184531A (zh) * 2010-05-07 2011-09-14 微软公司 深度图置信过滤
CN103139577A (zh) * 2011-11-23 2013-06-05 华为技术有限公司 一种深度图像滤波方法、获取深度图像滤波阈值的方法和装置
US9471963B2 (en) * 2009-01-21 2016-10-18 Samsung Electronics Co., Ltd. Method, medium, and apparatus of filtering depth noise using depth information

Family Cites Families (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101308573B (zh) * 2008-06-30 2011-07-13 北京中星微电子有限公司 一种消除噪声的方法和装置
US20120158496A1 (en) * 2010-12-20 2012-06-21 Amiad Solomon System and method for classifying webpages
KR20120084216A (ko) * 2011-01-19 2012-07-27 삼성전자주식회사 깊이 정보에 대한 픽셀 노이즈를 제거하는 3차원 이미지 신호 처리 방법 및 상기 방법을 수행하는 3차원 이미지 신호 프로세서
CN102279263B (zh) * 2011-05-06 2014-06-18 杭州顿力医疗器械股份有限公司 一种ccd型胶体金免疫层析诊断试纸定量分析系统
KR101862199B1 (ko) * 2012-02-29 2018-05-29 삼성전자주식회사 원거리 획득이 가능한 ｔｏｆ카메라와 스테레오 카메라의 합성 시스템 및 방법
CN102663696B (zh) * 2012-03-31 2015-03-25 广东威创视讯科技股份有限公司 放大图像的去噪方法和系统
KR101362183B1 (ko) * 2012-11-29 2014-02-13 한국과학기술연구원 카메라 자세 기반의 깊이 영상 노이즈 제거 장치 및 방법
KR101896301B1 (ko) * 2013-01-03 2018-09-07 삼성전자주식회사 깊이 영상 처리 장치 및 방법
JP6169361B2 (ja) * 2013-01-29 2017-07-26 フクダ電子株式会社 超音波診断装置および輝度補正方法
US10122993B2 (en) * 2013-03-15 2018-11-06 Fotonation Limited Autofocus system for a conventional camera that uses depth information from an array camera
US9786062B2 (en) * 2013-05-06 2017-10-10 Disney Enterprises, Inc. Scene reconstruction from high spatio-angular resolution light fields
JP6042030B2 (ja) * 2014-03-28 2016-12-14 富士フイルム株式会社 画像処理装置、撮影装置、画像処理方法、及びプログラム
CN103886557A (zh) * 2014-03-28 2014-06-25 北京工业大学 一种深度图像的去噪方法
CN104601861B (zh) * 2015-02-03 2017-08-29 河海大学常州校区 一种针对光纤监控视频序列的降噪方法及系统
US9501830B2 (en) * 2015-03-18 2016-11-22 Intel Corporation Blob detection in noisy images
CN104751491B (zh) * 2015-04-10 2018-01-23 中国科学院宁波材料技术与工程研究所 一种人群跟踪及人流量统计方法及装置
CN105374030B (zh) * 2015-10-12 2017-12-15 北京深视科技有限公司 一种背景模型和运动物体检测方法及系统
CN105225244A (zh) * 2015-10-22 2016-01-06 天津大学 基于最小局部均方差计算的噪点检测方法
CN105354805B (zh) * 2015-10-26 2020-03-06 京东方科技集团股份有限公司 深度图像的去噪方法和去噪设备
CN105787906A (zh) * 2016-03-25 2016-07-20 北京环境特性研究所 一种剔除红外图像亮噪点的方法及系统
CN106355561B (zh) * 2016-08-30 2019-11-15 天津大学 基于噪点先验约束的全变分图像去噪方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9471963B2 (en) * 2009-01-21 2016-10-18 Samsung Electronics Co., Ltd. Method, medium, and apparatus of filtering depth noise using depth information
CN102184531A (zh) * 2010-05-07 2011-09-14 微软公司 深度图置信过滤
CN103139577A (zh) * 2011-11-23 2013-06-05 华为技术有限公司 一种深度图像滤波方法、获取深度图像滤波阈值的方法和装置

Also Published As

Publication number Publication date
US20200151855A1 (en) 2020-05-14
US11238563B2 (en) 2022-02-01
CN109242782B (zh) 2022-09-09
CN109242782A (zh) 2019-01-18

Similar Documents

Publication Publication Date Title
EP3553465A1 (en) Information processing device and information processing method
WO2019052329A1 (zh) 人脸识别方法及相关产品
RU2533628C2 (ru) Устройство обработки информации, способ обработки информации и программа
US11151700B2 (en) Image processing method, terminal, and non-transitory computer-readable storage medium
KR20160115932A (ko) 알려진 타겟 오브젝트에 대응하는 피처들을 포함하는 피처 데이터베이스의 동적 업데이트
CN108141543A (zh) 基于预测环境因素的相机设置调整和采用这种相机设置调整的跟踪系统
KR102524982B1 (ko) 흐림 처리가 수행된 이미지에 노이즈 패턴을 반영하는 방법 및 장치
CN104613930A (zh) 一种测距的方法、装置及移动终端
CN103871051A (zh) 图像处理方法、装置和电子设备
CN113950820B (zh) 用于像素到像素信号扩散的校正
US11467673B2 (en) Method for controlling camera and electronic device therefor
CN110726971B (zh) 可见光定位方法、装置、终端及存储介质
KR20160038460A (ko) 전자 장치와, 그의 제어 방법
WO2021056501A1 (zh) 提取特征点的方法、可移动平台及存储介质
US20210117708A1 (en) Method for obtaining face data and electronic device therefor
CN114862828B (zh) 光斑搜索方法装置、计算机可读介质和电子设备
WO2019011017A1 (zh) 噪点处理方法及装置
US9912846B2 (en) Obtaining calibration data of a camera
US12069382B2 (en) Electronic device and method for generating images by performing auto white balance
CN113542597B (zh) 对焦方法以及电子设备
CN109643455A (zh) 摄像头标定方法和终端
EP2888716B1 (en) Target object angle determination using multiple cameras
EP3416130A1 (en) Method, device and nonvolatile computer-readable medium for image composition
CN112785650B (zh) 一种相机参数标定方法及装置
CN115243371B (zh) 一种定位方法、装置、电子设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18832326

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18832326

Country of ref document: EP

Kind code of ref document: A1