WO2017088391A1 - 视频去噪与细节增强方法及装置 - Google Patents

视频去噪与细节增强方法及装置 Download PDF

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WO2017088391A1
WO2017088391A1 PCT/CN2016/083054 CN2016083054W WO2017088391A1 WO 2017088391 A1 WO2017088391 A1 WO 2017088391A1 CN 2016083054 W CN2016083054 W CN 2016083054W WO 2017088391 A1 WO2017088391 A1 WO 2017088391A1
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pixel
pixel value
pixel point
point
value
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PCT/CN2016/083054
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English (en)
French (fr)
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刘阳
白茂生
魏伟
蔡砚刚
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乐视控股（北京）有限公司
乐视云计算有限公司
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Priority to RU2016136521A priority Critical patent/RU2016136521A/ru
Priority to US15/246,374 priority patent/US20170150014A1/en
Publication of WO2017088391A1 publication Critical patent/WO2017088391A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/80—Camera processing pipelines; Components thereof
    • H04N23/81—Camera processing pipelines; Components thereof for suppressing or minimising disturbance in the image signal generation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/14—Picture signal circuitry for video frequency region
    • H04N5/21—Circuitry for suppressing or minimising disturbance, e.g. moiré or halo

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  • Embodiments of the present invention relate to the field of video technologies, and in particular, to a video denoising and detail enhancement method and apparatus.
  • Video denoising methods can be basically divided into time domain denoising, spatial domain denoising, and time domain plus spatial domain denoising.
  • video denoising can remove the noise inside the picture, it will be accompanied by the loss of picture detail, which makes the denoised picture appear blurred.
  • the embodiment of the invention provides a video denoising and detail enhancement method and device, which are used to solve the defect that the user needs to manually switch the video output mode in the prior art, and realize automatic switching of the video output mode.
  • Embodiments of the present invention provide a video denoising and detail enhancement method, including:
  • the current pixel point is the detail pixel point, calculating a detail pixel value of the current pixel point according to the first pixel value and the second pixel value, and updating the detail pixel value The first pixel value.
  • Embodiments of the present invention provide a video denoising and detail enhancement apparatus, including:
  • a pixel point obtaining module configured to acquire a pixel value of a current pixel point in the frame as a first pixel value, and acquire a pixel value of an adjacent pixel point of the upper, lower, left, and right sides of the current pixel point; Pixel values of the Nth pixel point that are adjacent to the pixel point and located after the current pixel point, acquire pixel values of the Mth pixel point that are in the same column as the pixel point and are located below the pixel point, and obtain the a pixel value of the (M, N)th pixel point of the Mth pixel point and in the same column as the Nth pixel point;
  • a denoising module configured to perform denoising on the current pixel point according to the first pixel value and pixel values of adjacent pixels on the upper, lower, left, and right sides to obtain a second pixel value of the current pixel point;
  • a determining module configured to determine, according to the first pixel value, a pixel value of the Mth pixel point, a pixel value of the Nth pixel point, and a pixel value of the (M, N)th pixel point Whether the current pixel point is a detail pixel point;
  • a detail enhancement module if it is determined that the current pixel point is the detail pixel point, calculating a detail pixel value of the current pixel point according to the first pixel value and the second pixel value, and using the detail pixel The value updates the first pixel value.
  • the video denoising and detail enhancement method and apparatus provided by the embodiments of the present invention achieve effective denoising and preserve and enhance the denoising of each pixel and enhance the detail of the pixel in the detail part of the video.
  • the image details of the video frame improve the quality of the video and give the user a good viewing experience.
  • Embodiment 1 is a technical flowchart of Embodiment 1 of the present invention.
  • FIG. 2 is a schematic diagram of pixel points of video denoising according to an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of a pixel point enhanced in detail according to an embodiment of the present invention.
  • FIG. 5 is a technical flowchart of Embodiment 3 of the present invention.
  • FIG. 6 is a schematic structural diagram of a device according to Embodiment 4 of the present invention.
  • a video denoising and detail enhancement method mainly includes the following steps:
  • Step 110 Obtain a pixel value of a current pixel in the frame as a first pixel value, and acquire pixel values of adjacent pixel points on the upper, lower, left, and right sides of the current pixel point;
  • the denoising method adopted in the embodiment of the present invention is based on the Gaussian denoising principle, and the Gaussian denoising is a linear smoothing denoising method, and the denoising process is actually a process of weighting and averaging each pixel in the image.
  • the value pixel value of each pixel is obtained by weighted averaging of itself and other pixel values in the neighborhood.
  • the pixel points adjacent to the upper, lower, left, and right sides of the pixel to be denominated are taken as their neighboring pixel points.
  • the embodiment of the present invention first acquires the pixel value of the pixel to be denoised and the pixel value of the pixel in the neighborhood.
  • the current pixel point pixel value is P(i, j)
  • the pixel value of the adjacent pixel point on the left side is P(i-1, j)
  • the pixel of the right adjacent pixel point is The value is P(i+1,j)
  • the pixel value of the pixel adjacent to the upper side is P(i, j-1)
  • the pixel value of the pixel adjacent to the lower side is P(i, j+1) .
  • Step 120 Denoising the current pixel point according to the first pixel value and pixel values of adjacent pixel points of the upper, lower, left, and right sides to obtain a second pixel value of the current pixel point;
  • the step is performed according to the first pixel value, a preset denoising weight of the current pixel point, a pixel value of adjacent pixels on the upper and lower sides, and adjacent sides of the upper, lower, left, and right sides
  • the denoising weights of the pixels are weighted and averaged to obtain the second pixel values.
  • Step 130 Acquire a pixel value of an Nth pixel point that is adjacent to the current pixel point and located after the current pixel point, and acquire an Mth pixel point that is in the same column as the pixel point and is located below the pixel point.
  • the detail enhancement process of the embodiment of the present invention first determines whether the current pixel is the pixel value of the pixel of the neighboring pixel of the current pixel point.
  • the pixel of the detail part of the image In this embodiment, pixel values of a plurality of pixel points on the right side and the lower side of the current pixel point are respectively taken as a determination basis, as shown in FIG. 3, P(i, j) is before denoising of the current pixel point.
  • P(i+N, j) is a pixel value of the Nth pixel point
  • P(i, j+M) is a pixel value of the Mth pixel point
  • P(i+N,j +M) is the pixel value of the (M, N)th pixel point.
  • Step 140 Determine, according to the first pixel value, the pixel value of the Mth pixel point, the pixel value of the Nth pixel point, and the pixel value of the (M, N)th pixel point. Whether the current pixel is a detail pixel;
  • the detail position corresponds to a region where the pixel values such as the contour and the edge fluctuate greatly
  • the non-detail position corresponds to a flat region where the pixel value fluctuates; therefore, the fluctuation of the pixel value of the adjacent pixel through the current pixel point , to determine the details of the pixel.
  • Step 150 If it is determined that the current pixel point is the detail pixel point, calculate a detail pixel value of the current pixel point according to the first pixel value and the second pixel value, and use the detail pixel value Updating the first pixel value.
  • the denoising process strongly suppresses the noise in the image, it also causes the smoothing of the pixel values of the minutiae in the process of image smoothing, thereby causing image blurring.
  • the first pixel value of the detail pixel point prior to denoising represents a pixel value that can represent image detail plus a certain noise pollution
  • the said detail pixel point after denoising represents the image detail after the noise is not contaminated and smoothed, so the embodiment of the present invention takes the first pixel value and the second pixel value as the difference to obtain the closest of the detail pixel point. Pixel values.
  • the detailed pixel value is calculated using the following formula:
  • P(i,j) is the first pixel value
  • P′(i,j) is the second pixel value
  • P′′(i,j) is the detailed pixel value
  • multiplication coefficient m and n are integers.
  • each pixel point traversal in the video frame to be denoised performs steps 110 to 150 until the denoising and the searching and enhancement of the detail pixel points are completed for all the pixel points.
  • denoising is performed for each pixel in the video frame, and at the same time, for the denoised video frame, the detailed part of the frame is searched for and the pixel of the detail part is enhanced, and the pixel is effectively Noise also preserves and enhances the image detail of the video frame, improving the quality of the video.
  • FIG. 4 is a technical flowchart of Embodiment 2 of the present invention.
  • a video denoising process is further implemented by the following steps:
  • Step 210 Obtain a pixel value of a current pixel in the frame as a first pixel value, and acquire a pixel value of an adjacent pixel on the upper, lower, left, and right sides of the current pixel point;
  • the current pixel point pixel value is P(i, j)
  • the pixel value of the adjacent pixel point on the left side is P(i-1, j)
  • the pixel of the right adjacent pixel point is The value is P(i+1,j)
  • the pixel value of the pixel adjacent to the upper side is P(i, j-1)
  • the pixel value of the pixel adjacent to the lower side is P(i, j+1) .
  • Step 220 Calculate denoising weights of adjacent pixels on the upper, lower, left, and right sides according to a preset standard deviation according to a preset standard deviation;
  • the denoising weights of the adjacent pixels on the upper, lower, left, and right sides are calculated by a normal distribution formula, and the normal distribution formula is as follows:
  • Equation 2 f x is a normal distribution function, x is a random variable, and ⁇ is a normal distribution standard deviation.
  • the difference between the pixel value of the current pixel point and the pixel value of the adjacent pixel points of the upper, lower, left, and right sides is a random variable x, and is calculated according to a preset standard deviation ⁇ .
  • the specific calculation method is as shown in the following formula:
  • Step 230 According to the first pixel value, a preset denoising weight of the current pixel point, a pixel value of adjacent pixels on the upper, lower, left, and right sides, and adjacent pixel points on the upper, lower, left, and right sides The denoising weights are weighted and averaged to obtain the second pixel value.
  • the denoising weight w m of the current pixel point is a preset value, and is generally selected according to actual test experience and noise intensity.
  • the value of w m usually represents the intensity of denoising. If the noise intensity of the current pixel is relatively large, the value of w m should be appropriately reduced, so that the influence of noise points in the denoising result can be reduced; if the noise of the current pixel is If the intensity is relatively small, the value of w m should be appropriately increased, so that the smoothing effect of the neighboring pixel points on the currently denoised pixel point can be reduced.
  • the denoising weight w m 4 of the current pixel point is taken.
  • N(i,j) [w m *P(i,j)+w l *P(i-1,j)+w r *P(i+1,j)+w t *P(i,j -1) + w b * P(i, j+1)] / (w m + w l + w r + w t + w b )
  • N(i, j) is an average value obtained by weighted averaging, that is, the second pixel value.
  • the embodiment of the present invention further implements an adaptive change of w m according to the noise intensity by detecting a noise intensity of the current pixel point.
  • the specific implementation is as follows:
  • Excellent S1 respectively obtaining pixel values of current pixel points in the view frame to be denoised and pixel values of pixel points in the same position in the previous previous frame as the current pixel point;
  • the pixel value of the pixel in the current frame is P(i, j)
  • the pixel value of the pixel at the same position in the adjacent previous frame is P'(i, j)
  • i, j is the coordinate of the pixel in the frame.
  • the acquisition in this step is performed by traversing all the pixels in the video frame.
  • Excellent S2 normalizing the obtained pixel value P(i, j) such that 0 ⁇ P ⁇ 1;
  • V(i,j) is the result of the normalization calculation
  • P(i,j) is the pixel value of each current pixel
  • 255 is the maximum value of the pixel value
  • 0 is the minimum value of the pixel value.
  • V(i,j) is the pixel value of the current pixel after the normalization process
  • V'(i,j) is the current and the current previous frame after the normalization process
  • m and n are constant and are empirical values, and are set in advance according to the degree of denoising. After testing, the value of n is between 0.80 and 0.99, and the adaptive denoising effect is optimal.
  • x and y are empirical values, adjusted according to the noise intensity of the current pixel point.
  • the noise intensity L(i,j) is greater than a certain threshold, the denoising weight of the current pixel point is reduced by decreasing x, y, thereby reducing the denoising weight of the noise point to Achieve better denoising effect.
  • the preferred steps of the embodiment of the present invention can implement adaptive denoising according to changes in noise intensity.
  • the preferred steps in this embodiment enable adaptive adjustment of the denoising strength, which is better preserved for video with or without noise.
  • FIG. 5 is a technical flowchart of Embodiment 3 of the present invention.
  • the process of detail enhancement is further implemented by the following steps:
  • Step 310 Acquire a pixel value of an Nth pixel point that is adjacent to the current pixel point and located after the current pixel point, and acquire an Mth pixel point that is in the same column as the pixel point and is located below the pixel point. a pixel value obtained by acquiring pixel values of the (M, N)th pixel points that are in the same column as the Mth pixel point and are in the same column as the Nth pixel point.
  • P(i, j) is the pixel value before denoising of the current pixel point
  • P(i+N, j) is a pixel value of the Nth pixel
  • P(i, j+M) is a pixel value of the Mth pixel
  • P(i+N, j+M) is the (M, N)th The pixel value of the pixel.
  • Step 320 Determine whether the current pixel point is the detail pixel point, if it is the detail pixel point, step 330 is performed;
  • the determining method is based on the first pixel value, the pixel value of the Mth pixel point, the pixel value of the Nth pixel point, and the pixel value of the (M, N)th pixel point. And if the first pixel value of the current pixel point satisfies the following formula, determining that the current pixel point is the detail pixel point:
  • Equation 6 P(i,j) is the first pixel value of the current pixel point, P(i+N,j) is the pixel value of the Nth pixel point, P(i,j+ M) is a pixel value of the Mth pixel, P(i+N, j+M) is a pixel value of the (M, N)th pixel, and S is a preset threshold.
  • the first pixel value of the current pixel point is P(i,j)
  • the Nth pixel is the third pixel after the same pixel of the current pixel, and the value is P(i+3, j);
  • the Mth pixel is the same pixel of the next row of the current pixel.
  • the value is P(i, j+1);
  • the (M, N)th pixel is the pixel of the third column of the next row of the current pixel, and its value is P(i+3,j +1).
  • the current pixel point P(i, j) is recorded as a detail pixel point.
  • Step 330 Calculate a detail pixel value of the current pixel point according to the first pixel value and the second pixel value.
  • P(i,j) is the first pixel value
  • P′(i,j) is the second pixel value
  • P′′(i,j) is the detailed pixel value
  • multiplication coefficient m and n are integers.
  • the pixel value can be calculated by substituting the pixel value and the pixel value after denoising The final detail pixel value, and the first pixel value is updated with the detail pixel value.
  • steps 310 to 320 are performed on each of the denoised pixels, so that some of the detailed pixel points are ignored and the image details are unclear.
  • whether the pixel point in the denoising process is a detail pixel point is determined by the pixel value of the current pixel point and the surrounding pixel point, and the detail pixel point is counted and enhanced in detail, and is preserved while denoising
  • the image details are enhanced to further optimize the quality of the video image and enhance the user's viewing experience.
  • FIG. 6 is a technical flowchart of Embodiment 4 of the present invention.
  • a video denoising and detail enhancement apparatus mainly includes the following modules: a pixel point acquisition module 610, a denoising module 620, and a determination module 630. , detail enhancement module 640.
  • the pixel point acquisition module 610 is configured to acquire a pixel value of a current pixel point in the frame as a first pixel value, and acquire pixel values of adjacent pixel points on the upper, lower, left, and right sides of the current pixel point; Pixel values of the Nth pixel point of the current pixel point and located after the current pixel point, acquiring pixel values of the Mth pixel point in the same column as the pixel point and located below the pixel point, and acquiring a pixel value of the (M, N)th pixel point that is adjacent to the Mth pixel point and is in the same column as the Nth pixel point;
  • the denoising module 620 is connected to the pixel point acquiring module 610, and configured to perform the current pixel point according to the first pixel value and pixel values of adjacent pixel points of the upper, lower, left, and right sides. Noise obtaining a second pixel value of the current pixel point;
  • the determining module 630 is connected to the pixel point acquiring module 610, and configured to use, according to the first pixel value, a pixel value of the Mth pixel point, a pixel value of the Nth pixel point, and a Determining, by the pixel value of the (M, N)th pixel point, whether the current pixel point is a detail pixel point;
  • the detail enhancement module 640 is connected to the denoising module 620 and the determining module 630, and if it is determined that the current pixel point is the detailed pixel point, according to the first pixel value and the second The pixel value calculates a detail pixel value of the current pixel point and updates the first pixel value with the detail pixel value.
  • the denoising module 620 is further configured to: according to the first pixel value, a preset denoising weight of the current pixel point, a pixel value of an adjacent pixel point of the upper, lower, left, and right sides, and the upper and lower The denoising weights of adjacent pixels on the left and right sides are weighted and averaged to obtain the second pixel value.
  • the denoising module 620 is further configured to calculate a denoising weight of the adjacent pixels on the upper, lower, left, and right sides according to a preset standard deviation according to a preset standard deviation.
  • the determining module 630 is further configured to determine that the current pixel point is the detail pixel point if the first pixel value of the current pixel point satisfies the following formula:
  • P(i,j) is the first pixel value of the current pixel point
  • P(i+N,j) is a pixel value of the Nth pixel point
  • P(i,j+M) For the pixel value of the Mth pixel
  • P(i+N, j+M) is the pixel value of the (M, N)th pixel
  • S is a preset threshold.
  • the detail enhancement module 640 is further configured to calculate the detail pixel value by using the following formula:
  • P(i,j) is the first pixel value
  • P′(i,j) is the second pixel value
  • P′′(i,j) is the detailed pixel value
  • multiplying coefficient m and n is an integer.
  • the apparatus shown in FIG. 6 can perform the methods of the embodiments shown in FIG. 1, FIG. 4, and FIG. 5, and the implementation principle and technical effects refer to the embodiments shown in FIG. 1, FIG. 4, and FIG. 5, and details are not described herein again.
  • the foregoing program may be stored in a computer readable storage medium, and the program is executed when executed.
  • the foregoing steps include the steps of the foregoing method embodiments; and the foregoing storage medium includes: a medium that can store program codes, such as a ROM, a RAM, a magnetic disk, or an optical disk.

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Abstract

一种视频去噪与细节增强方法及装置。获取帧内当前像素点的像素值为第一像素值，并获取所述当前像素点上下左右四侧的相邻像素点的像素值；对所述当前像素点进行去噪得到所述当前像素点的第二像素值；判断所述当前像素点是否为细节像素点；若判定所述当前像素点为所述细节像素点，则根据所述第一像素值以及所述第二像素值计算所述当前像素点的细节像素值，并以所述细节像素值更新所述第一像素值。实现了有效的去噪同时保留并增强了视频帧的图像细节。

Description

视频去噪与细节增强方法及装置
交叉引用
本申请引用于2015年11月24日递交的名称为“视频去噪与细节增强方法及装置”的第201510828893.3号中国专利申请，其通过引用被全部并入本申请。
技术领域
本发明实施例涉及视频技术领域，尤其涉及一种视频去噪与细节增强方法及装置。
背景技术
随着数字视频应用的迅猛发展，在数字视频系统中，视频的采集、传输、编码、解码等过程会不可避免地引入各种噪声，噪声的存在不但严重影响了视频主观视觉质量，而且会影响视频的后续处理，例如编码、转码等。因此，伴随着数字视频的广泛应用，迫切需要有高效的视频去噪方法。
视频的去噪方法基本上可以分为时间域去噪、空间域去噪和时间域加空间域去噪等类型。视频去噪虽然可以去除画面内的噪声，但同时会伴随着画面细节的损失，使得去噪后的画面出现模糊的情况。
因此，一种视频去噪与细节增强的方法亟待提出。
发明内容
本发明实施例提供一种视频去噪与细节增强方法及装置，用以解决现有技术中用户需要手动按键切换视频输出模式的缺陷，实现视频输出模式的自动切换。
本发明实施例提供一种视频去噪与细节增强方法，包括：
获取帧内当前像素点的像素值为第一像素值，并获取所述当前像素点上下左右四侧的相邻像素点的像素值；
根据所述第一像素值以及所述上下左右四侧的相邻像素点的像素值对所述当前像素点进行去噪得到所述当前像素点的第二像素值；
获取与所述当前像素点同行且位于所述当前像素点之后的第N个像素点的像素值，获取与所述像素点同列且位于所述像素点之下的第M个像素点的像素值，获取与所述第M个像素点同行且与所述第N个像素点同列的第(M,N)个像素点的像素值；
根据所述第一像素值、所述第M个像素点的像素值、所述第N个像素点的像素值以及所述第(M,N)个像素点的像素值判断所述当前像素点是否为细节像素点；
若判定所述当前像素点为所述细节像素点，则根据所述第一像素值以及所述第二像素值计算所述当前像素点的细节像素值，并以所述细节像素值更新所述第一像素值。
本发明实施例提供一种视频去噪与细节增强装置，包括：
像素点获取模块，用于获取帧内当前像素点的像素值为第一像素值，并获取所述当前像素点上下左右四侧的相邻像素点的像素值；还用于获取与所述当前像素点同行且位于所述当前像素点之后的第N个像素点的像素值，获取与所述像素点同列且位于所述像素点之下的第M个像素点的像素值，获取与所述第M个像素点同行且与所述第N个像素点同列的第(M,N)个像素点的像素值；
去噪模块，用于根据所述第一像素值以及所述上下左右四侧的相邻像素点的像素值对所述当前像素点进行去噪得到所述当前像素点的第二像素值；
判断模块，用于根据所述第一像素值、所述第M个像素点的像素值、所述第N个像素点的像素值以及所述第(M,N)个像素点的像素值判断所述当前像素点是否为细节像素点；
细节增强模块，若判定所述当前像素点为所述细节像素点，则根据所述第一像素值以及所述第二像素值计算所述当前像素点的细节像素值，并以所述细节像素值更新所述第一像素值。本发明实施例提供的视频去噪与细节增强方法及装置，通过对每个像素点进行去噪并对视频中的细节部分的像素点进行细节增强，实现了有效的去噪并且保留并增强了视频帧的图像细节，提高了视频的质量，给用户带来了良好的观看体验。
附图说明
图1为本发明实施例一的技术流程图；
图2为本发明实施例一视频去噪的像素点示意图；
图3为本发明实施例一细节增强的像素点示意图；
图4为本发明实施例二的技术流程图；
图5为本发明实施例三的技术流程图；
图6为本发明实施例四的装置结构示意图。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
实施例一
图1是本发明实施例一的技术流程图，结合图1，本发明实施例一种视频去噪与细节增强方法，主要包括如下的步骤：
步骤110：获取帧内当前像素点的像素值为第一像素值，并获取所述当前像素点上下左右四侧的相邻像素点的像素值；
本发明实施例采用的去噪方法基于高斯去噪原理，高斯去噪是一种线性平滑去噪方式，其去噪过程实际上是对图像中的每个像素点进行加权平均的过程。在高斯去噪的过程中，每一个像素点的值像素值都由其本身和邻域内的其他像素值经过加权平均后得到。在本发明实施例中，取当前待去噪像素点的上下左右四侧相邻的像素点作为其邻域像素点。
因此，本发明实施例在去噪过程中，首先获取当前待去噪像素点的像素值及其邻域内的像素点的像素值。如图2所示，所述当前像素点像素值为P(i,j)，左侧相邻的像素点的像素值为P(i-1,j)，右侧相邻的像素点的像素值为P(i+1,j)，上侧相邻的像素点的像素值为P(i,j-1)，下侧相邻的像素点的像素值为P(i,j+1)。
步骤120：根据所述第一像素值以及所述上下左右四侧的相邻像素点的像素值对所述当前像素点进行去噪得到所述当前像素点的第二像素值；
具体地，本步骤根据所述第一像素值、预设的所述当前像素点的去噪权重、所述上下左右四侧的相邻像素点的像素值以及所述上下左右四侧的相邻像素点的去噪权重进行加权求平均得到所述第二像素值。
步骤130：获取与所述当前像素点同行且位于所述当前像素点之后的第N个像素点的像素值，获取与所述像素点同列且位于所述像素点之下的第M个像素点的像素值，获取与所述第M个像素点同行且与所述第N个像素点同列的第(M,N)个像素点的像素值；
当完成帧内所有像素点的去噪后，便进行细节增强处理，本发明实施例的细节增强过程首先利用所述当前像素点某侧邻域像素点的像素值判断所述当前像素点是否为图像中细节部分的像素点。本实施例中，分别取所述当前像素点右侧和下侧的若干像素点的像素值作为判断依据，如图3所示，P(i,j)为所述当前像素点的去噪之前像素值，P(i+N,j)为所述第N个像素点的像素值，P(i,j+M)为所述第M个像素点的像素值，P(i+N,j+M)为所述第(M,N)个像素点的像素值。
步骤140：根据所述第一像素值、所述第M个像素点的像素值、所述第N个像素点的像素值以及所述第(M,N)个像素点的像素值判断所述当前像素点是否为细节像素点；
由于在图像中，细节位置对应的是轮廓、边缘等像素值波动较大的区域，而非细节位置对应着像素值波动较平坦区域；因此通过当前像素点的相邻像素点像素值的波动情况，便可以确定细节像素点。
步骤150：若判定所述当前像素点为所述细节像素点，则根据所述第一像素值以及所述第二像素值计算所述当前像素点的细节像素值，并以所述细节像素值更新所述第一像素值。
尽管去噪的过程有力地抑制了图像中的噪声，但是也在图像平滑的过程中造成了细节点像素值的平滑，从而引起图像模糊现象。对于每个细节像素点，在去噪之前所述细节像素点的所述第一像素值代表的是能表现图像细节的像素值附加一定的噪声污染，去噪之后所述细节像素点的所述第二像素值代表了不带噪声污染且被平滑之后的图像细节，因此本发明实施例采取所述第一像素值和所述第二像素值作差的方式获取所述细节像素点最接近的像素值。
具体地，采用如下的公式计算所述细节像素值：
P″(i,j)＝m*P(i,j)-n*P′(i,j)     公式1
公式1中，P(i,j)为所述第一像素值，P′(i,j)为所述第二像素值，P″(i,j)为所述细节像素值，倍乘系数m和n为整数。
需要说明的是，对待去噪的视频帧内的每一个像素点遍历执行步骤110～步骤150，直至对所有像素点完成去噪以及细节像素点的寻找和增强。
本实施例中，针对视频帧内的每一像素点进行去噪，与此同时，针对去噪后的视频帧，寻找帧内的细节部分并对细节部分的像素点进行增强，在有效的去噪同时保留并增强了视频帧的图像细节，提高了视频的质量。
实施例二
图4是本发明实施例二的技术流程图，结合图4，本发明实施例一种视频去噪与细节增强方法中，视频去噪的过程进一步由以下的步骤实现：
步骤210：获取帧内当前像素点的像素值为第一像素值，并获取所述当前像素点上下左右四侧的相邻像素点的像素值；
如图2所示，所述当前像素点像素值为P(i,j)，左侧相邻的像素点的像素值为P(i-1,j)，右侧相邻的像素点的像素值为P(i+1,j)，上侧相邻的像素点的像素值为P(i,j-1)，下侧相邻的像素点的像素值为P(i,j+1)。
步骤220：根据预设的标准差，以正态分布公式计算所述上下左右四侧的相邻像素点的去噪权重；
本发明实施例中，所述上下左右四侧的相邻像素点的去噪权重以正态分布公式进行计算，正态分布公式如下所示：
Figure PCTCN2016083054-appb-000001
公式2中fx是正态分布函数，x是随机变量，σ为正态分布标准差。
本发明实施例中，以所述当前像素点的像素值分别与所述上下左右四侧的相邻像素点的像素值的差值为随机变量x，根据预设的标准差σ进行计算。具体计算方法如以下公式所示：
xl＝[P(i-1,j)-P(i,j)
xr＝P(i+1,j)-P(i,j)
xt＝P(i,j-1)-P(i,j)
xb＝P(i,j+1)-P(i,j)
Figure PCTCN2016083054-appb-000002
Figure PCTCN2016083054-appb-000003
Figure PCTCN2016083054-appb-000004
Figure PCTCN2016083054-appb-000005
公式3中，xl、xr、xt、xb分别是所述当前像素点的像素值与所述上下左右四侧的相邻像素点的像素值的差值，wl是所述左侧相邻的像素点的去噪权重，wr是所述右侧相邻的像素点的去噪权重，wt是所述上侧相邻的像素点的去噪权重，wb是所述下侧相邻的像素点的去噪权重，σ是预设的标准差，一般地，根据经验，取σ＝10。
步骤230：根据所述第一像素值、预设的所述当前像素点的去噪权重、所述上下左右四侧的相邻像素点的像素值以及所述上下左右四侧的相邻像素点的去噪权重进行加权求平均得到所述第二像素值。
本发明实施例中，所述当前像素点的去噪权重wm为预设值，通常根据实际测试经验以及噪声强度进行选设置。wm的值通常代表了去噪的强度，若是当前像素点的噪声强度比较大，则应适当减小wm的值，从而可以减少去噪结果中噪声点的影响；若是当前像素点的噪声强度比较小，则应适当增大wm的值，从而可以减少邻域像素点对当前被去噪像素点的平滑作用。本实施例中，取所述当前像素点的去噪权重wm＝4。
加权求平均的具体计算如下公式所示：
N(i,j)＝[wm*P(i,j)+wl*P(i-1,j)+wr*P(i+1,j)+wt*P(i,j-1)+wb*P(i,j+1)]/(wm+wl+wr+wt+wb)       公式4
公式4中，N(i,j)是加权求平均得到的平均值，即所述第二像素值。
优选地，本发明实施例进一步可通过检测所述当前像素点的噪声强度从而根据所述噪声强度实现wm的自适应变化。具体实现如下：
优S1：分别获取待去噪的视帧内的当前像素点的像素值与相邻前一帧中与所述当前像素点相同位置处的像素点的像素值；
如图2示，所述当前帧中的像素点的像素值为P(i,j)，所述相邻前一帧中相同位置处的像素点的像素值为P’(i,j)，i,j是像素点在所在帧内的坐标，本步骤中的获取对视频帧内的所有像素点均遍历执行。
优S2：对获取到的像素值P(i,j)进行归一化处理，使得0≤P≤1；
归一化计算的具体公式如下：
Figure PCTCN2016083054-appb-000006
公式5中，V(i,j)是归一化计算的结果，P(i,j)是每个当前像素点的像素值，255是像素值的最大值，0是像素值的最小值。
优S3：使用公式L(i,j)＝(m*(1-|V’(i,j)-V(i,j)|))n*|V’(i,j)-V(i,j)|计算所述当前像素点的噪声强度；
其中V(i,j)是归一化处理后的所述当前像素点的像素值，V’(i,j)是归一化处理后的所述相邻前一帧中的与所述当前像素点位置相同的像素点的像素值，m和n是常数且均为经验值，根据去噪程度进行预先设置。经测试研究，n的取值范围在0.80～0.99之间时，自适应的去噪效果最优。
优S4：根据公式wm＝x+y*L(i,j)计算所述当前像素点的去噪权重.
其中x和y是经验值，根据所述当前像素点的所述噪声强度进行调整。当所述噪声强度L(i,j)大于某一特定的阈值时，通过减小x，y来减小所述当前像素点的所述去噪权重，从而减小噪声点的去噪权重以达到较好的去噪效果。
通过执行步骤优S1～优S4，本发明实施例的优选步骤可以实现根据噪声强度变化的自适应去噪。
需要说明的是，实施例三的所有步骤对待去噪视频帧内的每一像素点遍历执行，以实现全帧去噪，具体重复执行过程此处不做赘述。
本实施例中，通过对视频帧内的每一像素点进行平滑，去除了视频画面的噪声，提高了视频的主观视觉质量，减弱了对后续视频处理过程的影响。与此同时，本实施例中的优选步骤实现了去噪强度的自适应调节，对于噪声强度变化或者没有噪声的视频能够较好地保留其帧内细节。
实施例三
图5是本发明实施例三的技术流程图，结合图5，本发明实施例一种视频去噪与细节增强方法中，细节增强的过程进一步由以下的步骤实现：
步骤310：获取与所述当前像素点同行且位于所述当前像素点之后的第N个像素点的像素值，获取与所述像素点同列且位于所述像素点之下的第M个像素点的像素值，获取与所述第M个像素点同行且与所述第N个像素点同列的第(M,N)个像素点的像素值。
如图3所示，P(i,j)为所述当前像素点的去噪之前像素值，P(i+N,j)为所 述第N个像素点的像素值，P(i,j+M)为所述第M个像素点的像素值，P(i+N,j+M)为所述第(M,N)个像素点的像素值。
步骤320：判断所述当前像素点是否为所述细节像素点，若为所述细节像素点，则执行步骤330；
具体地，判断方法是根据所述第一像素值、所述第M个像素点的像素值、所述第N个像素点的像素值以及所述第(M,N)个像素点的像素值，若所述当前像素点的所述第一像素值满足如下的公式，则判定所述当前像素点为所述细节像素点：
Figure PCTCN2016083054-appb-000007
公式6中，P(i,j)为所述当前像素点的所述第一像素值，P(i+N,j)为所述第N个像素点的像素值，P(i,j+M)为所述第M个像素点的像素值，P(i+N,j+M)为所述第(M,N)个像素点的像素值，S为预设的阈值。
本实施例中，根据大量的实验经验，取N＝3，M＝1，S＝10，所述当前像素点的所述第一像素值为P(i,j)，所述第N个像素点为所述当前像素点同一行的之后第3个像素点，其值为P(i+3,j)；所述第M个像素点为所述当前像素点下一行的同列的像素点，其值为P(i,j+1)；所述第(M,N)个像素点为所述当前像素点下一行的后面第三列的像素点，其值为P(i+3,j+1)。将N＝3，M＝1，S＝10代入公式6：
Figure PCTCN2016083054-appb-000008
若上述四个像素点的值满足上述公式6’，则将当前像素点P(i,j)记为细节像素点。
步骤330：根据所述第一像素值以及所述第二像素值计算所述当前像素点的细节像素值。
采用如下的公式计算所述细节像素值：
P″(i,j)＝m*P(i,j)-n*P′(i,j)     公式1
公式1中，P(i,j)为所述第一像素值，P′(i,j)为所述第二像素值，P″(i,j)为所述细节像素值，倍乘系数m和n为整数。
本实施例中，取m＝2，n＝1，公式1为P″(i,j)＝2*P(i,j)-P′(i,j)，将当前像素点去噪之前的像素值和去噪之后的像素值代入即可计算出所述像素点 的最终细节像素值，并以所述细节像素值更新所述第一像素值。
需要说明的是，步骤310～步骤320对每一个去噪后的像素点均执行，从而能避免部分细节像素点被忽略导致图像细节不清楚。
本实施例中，通过当前像素点及其周围像素点的像素值判断去噪处理后的帧内像素点是否为细节像素点，统计所述细节像素点并进行细节增强，在去噪的同时保留并增强了图像细节，进一步优化了视频画面质量，提升了用户观看体验。
实施例四
图6是本发明实施例四的技术流程图，结合图6，本发明实施例一种视频去噪与细节增强装置主要包括如下的模块：像素点获取模块610、去噪模块620、判断模块630、细节增强模块640。
所述像素点获取模块610，用于获取帧内当前像素点的像素值为第一像素值，并获取所述当前像素点上下左右四侧的相邻像素点的像素值；还用于获取与所述当前像素点同行且位于所述当前像素点之后的第N个像素点的像素值，获取与所述像素点同列且位于所述像素点之下的第M个像素点的像素值，获取与所述第M个像素点同行且与所述第N个像素点同列的第(M,N)个像素点的像素值；
所述去噪模块620，与所述像素点获取模块610相连接，用于根据所述第一像素值以及所述上下左右四侧的相邻像素点的像素值对所述当前像素点进行去噪得到所述当前像素点的第二像素值；
所述判断模块630，与所述像素点获取模块610相连接，用于根据所述第一像素值、所述第M个像素点的像素值、所述第N个像素点的像素值以及所述第(M,N)个像素点的像素值判断所述当前像素点是否为细节像素点；
所述细节增强模块640，与所述去噪模块620以及所述判断模块630相连接，若判定所述当前像素点为所述细节像素点，则根据所述第一像素值以及所述第二像素值计算所述当前像素点的细节像素值，并以所述细节像素值更新所述第一像素值。
所述去噪模块620，进一步用于，根据所述第一像素值、预设的所述当前像素点的去噪权重、所述上下左右四侧的相邻像素点的像素值以及所述上下左右四侧的相邻像素点的去噪权重进行加权求平均得到所述第二像素值。
所述去噪模块620，进一步还用于，根据预设的标准差，以正态分布公式计算所述上下左右四侧的相邻像素点的去噪权重。
所述判断模块630，进一步用于，若所述当前像素点的所述第一像素值满足如下的公式，则判定所述当前像素点为所述细节像素点：
Figure PCTCN2016083054-appb-000009
其中，P(i,j)为所述当前像素点的所述第一像素值，P(i+N,j)为所述第N个像素点的像素值，P(i,j+M)为所述第M个像素点的像素值，P(i+N,j+M)为所述第(M,N)个像素点的像素值，S为预设的阈值。
所述细节增强模块640，进一步用于，采用如下的公式计算所述细节像素值：
P″(i,j)＝m*P(i,j)-n*P′(i,j)
其中，P(i,j)为所述第一像素值，P′(i,j)为所述第二像素值，P″(i,j)为所述细节像素值，倍乘系数m和n为整数。
图6所示装置可以执行图1、图4以及图5所示实施例的方法，实现原理和技术效果参考图1、图4以及图5所示实施例，不再赘述。
本领域普通技术人员可以理解：实现上述方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成，前述的程序可以存储于一计算机可读取存储介质中，该程序在执行时，执行包括上述方法实施例的步骤；而前述的存储介质包括：ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。
最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。

Claims (10)

  1. 一种视频去噪与细节增强方法，其特征在于，包括：
    获取帧内当前像素点的像素值为第一像素值，并获取所述当前像素点上下左右四侧的相邻像素点的像素值；
    根据所述第一像素值以及所述上下左右四侧的相邻像素点的像素值对所述当前像素点进行去噪得到所述当前像素点的第二像素值；
    获取与所述当前像素点同行且位于所述当前像素点之后的第N个像素点的像素值，获取与所述像素点同列且位于所述像素点之下的第M个像素点的像素值，获取与所述第M个像素点同行且与所述第N个像素点同列的第(M,N)个像素点的像素值；
    根据所述第一像素值、所述第M个像素点的像素值、所述第N个像素点的像素值以及所述第(M,N)个像素点的像素值判断所述当前像素点是否为细节像素点；
    若判定所述当前像素点为所述细节像素点，则根据所述第一像素值以及所述第二像素值计算所述当前像素点的细节像素值，并以所述细节像素值更新所述第一像素值。
  2. 根据权利要求1所述的方法，其特征在于，得到所述当前像素点的第二像素值，进一步包括：
    根据所述第一像素值、预设的所述当前像素点的去噪权重、所述上下左右四侧的相邻像素点的像素值以及所述上下左右四侧的相邻像素点的去噪权重进行加权求平均得到所述第二像素值。
  3. 根据权利要求2所述的方法，其特征在于，所述方法进一步包括：
    根据预设的标准差，以正态分布公式计算所述上下左右四侧的相邻像素点的去噪权重。
  4. 根据权利要求1所述的方法，其特征在于，判断所述当前像素点是否为细节像素点，进一步包括：
    若所述当前像素点的所述第一像素值满足如下的公式，则判定所述当前像素点为所述细节像素点：
    Figure PCTCN2016083054-appb-100001
    其中，P(i,j)为所述当前像素点的所述第一像素值，P(i+N,j)为所述第N个像素点的像素值，P(i,j+M)为所述第M个像素点的像素值，P(i+N,j+M)为所述第(M,N)个像素点的像素值，S为预设的阈值。
  5. 根据权利要求1所述的方法，其特征在于，根据所述第一像素值以及所述第二像素值计算所述当前像素点的细节像素值，进一步包括：
    采用如下的公式计算所述细节像素值：
    P″(i,j)＝m*P(i,j)-n*P′(i,j)
    其中，P(i,j)为所述第一像素值，P′(i,j)为所述第二像素值，P″(i,j)为所述细节像素值，倍乘系数m和n为整数。
  6. 一种视频去噪与细节增强装置，其特征在于，包括：
    像素点获取模块，用于获取帧内当前像素点的像素值为第一像素值，并获取所述当前像素点上下左右四侧的相邻像素点的像素值；还用于获取与所述当前像素点同行且位于所述当前像素点之后的第N个像素点的像素值，获取与所述像素点同列且位于所述像素点之下的第M个像素点的像素值，获取与所述第M个像素点同行且与所述第N个像素点同列的第(M,N)个像素点的像素值；
    去噪模块，用于根据所述第一像素值以及所述上下左右四侧的相邻像素点的像素值对所述当前像素点进行去噪得到所述当前像素点的第二像素值；
    判断模块，用于根据所述第一像素值、所述第M个像素点的像素值、所述第N个像素点的像素值以及所述第(M,N)个像素点的像素值判断所述当前像素点是否为细节像素点；
    细节增强模块，若判定所述当前像素点为所述细节像素点，则根据所述第一像素值以及所述第二像素值计算所述当前像素点的细节像素值，并以所述细节像素值更新所述第一像素值。
  7. 根据权利要求6所述的装置，其特征在于，所述去噪模块，进一步用于：
    根据所述第一像素值、预设的所述当前像素点的去噪权重、所述上下左右四侧的相邻像素点的像素值以及所述上下左右四侧的相邻像素点的去噪权重进行加权求平均得到所述第二像素值。
  8. 根据权利要求7所述的装置，其特征在于，所述去噪模块，进一步用 于：
    根据预设的标准差，以正态分布公式计算所述上下左右四侧的相邻像素点的去噪权重。
  9. 根据权利要求6所述的装置，其特征在于，所述判断模块，进一步用于：
    若所述当前像素点的所述第一像素值满足如下的公式，则判定所述当前像素点为所述细节像素点：
    Figure PCTCN2016083054-appb-100002
    其中，P(i,j)为所述当前像素点的所述第一像素值，P(i+N,j)为所述第N个像素点的像素值，P(i,j+M)为所述第M个像素点的像素值，P(i+N,j+M)为所述第(M,N)个像素点的像素值，S为预设的阈值。
  10. 根据权利要求6所述的装置，其特征在于，所述细节增强模块，进一步用于：
    采用如下的公式计算所述细节像素值：
    P″(i,j)＝m*P(i,j)-n*P′(i,j)
    其中，P(i,j)为所述第一像素值，P′(i,j)为所述第二像素值，P″(i,j)为所述细节像素值，倍乘系数m和n为整数。
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