WO2018121221A1 - 一种解决通道间亮度不一致的方法 - Google Patents

一种解决通道间亮度不一致的方法 Download PDF

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WO2018121221A1
WO2018121221A1 PCT/CN2017/115207 CN2017115207W WO2018121221A1 WO 2018121221 A1 WO2018121221 A1 WO 2018121221A1 CN 2017115207 W CN2017115207 W CN 2017115207W WO 2018121221 A1 WO2018121221 A1 WO 2018121221A1
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brightness
model
seam
pixels
optimal solution
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PCT/CN2017/115207
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French (fr)
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李赟晟
王勇
王凯
叶红磊
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上海集成电路研发中心有限公司
成都微光集电科技有限公司
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformations in the plane of the image
    • G06T3/40Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4038Image mosaicing, e.g. composing plane images from plane sub-images
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10004Still image; Photographic image
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20212Image combination
    • G06T2207/20221Image fusion; Image merging

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  • the present invention relates to the field of image sensor technologies, and in particular, to a method for solving brightness inconsistency between channels.
  • the conventional method is to add a value to a channel. Make the brightness between the two channels consistent, or adjust the gain between the two channels, so that one channel is multiplied by a value to get the same brightness, or feathered at the stitching gap, so that it looks smooth and excessive, the brightness change does not look obvious .
  • the first two methods are mainly for the case where the brightness of the two channels is consistent, and the effect of the brightness variation between the channels is inconsistent, that is, the effect may be different when the upper half and the lower half are different, and the last feathering effect is not obvious. Affecting the sharpness of the image, it is not feasible to face high definition.
  • the object of the present invention is to remedy the above deficiencies of the prior art, and the present invention aims to provide a method for solving the inconsistency of brightness between channels, which can automatically identify the optimal solution of the value change between channels and improve the map. Like an effect.
  • the present invention provides a method for solving brightness inconsistency between channels, which comprises the following steps:
  • Step 01 providing two images, at a seam of the two images, setting a width range; the seam is surrounded by the set width;
  • Step 02 Calculate the brightness of each line of pixels in the set width range
  • Step 03 taking the coordinates of the pixel values of each row in the set width range as the abscissa and the ordinate, thereby obtaining a point set;
  • Step 04 According to the method of obtaining the optimal solution in the Ransac algorithm, the point set is fitted to a straight line to obtain an optimal solution model of the point set;
  • Step 05 Find a fitting value of each pixel in one side of the seam in the optimal solution model, and adjust the brightness of the pixel at the corresponding position on the side of the seam according to the fitting value.
  • the brightness of each row of pixels is the sum of each pixel value in the corresponding row divided by the set width.
  • the brightness of each row of pixels to the left of the seam is equal to the brightness of the pixels of the corresponding row to the right of the seam.
  • the step 04 specifically includes:
  • Step 041 Set a model with a minimum sampling set of n and a sample set P.
  • the sample set P is a set of points with the pixel values in each row as the horizontal and vertical coordinates, and the number of samples in the sample set P is greater than n;
  • n is The minimum number of samples required to initialize the model M i parameters, n ⁇ 1;
  • Step 042 Randomly extract a subset S of the sample set P containing n samples from the sample set P, and simulate to obtain an initialization model M i .
  • the corresponding initialization model (M i ) is the optimal solution model.
  • the initialization model (M i ) is obtained by a least squares method.
  • the step 05 specifically includes: matching all pixels on the left side or the right side of the patch to corresponding pixels in the optimal solution model, thereby completing brightness adjustment.
  • the method for solving the inconsistency of brightness between channels can automatically compensate for the difference in brightness between channels and optimize the image effect.
  • FIG. 1 is a schematic diagram of an image of a channel inconsistency according to a preferred embodiment of the present invention
  • FIG. 2 is a schematic diagram of an optimal solution model according to a preferred embodiment of the present invention.
  • FIG. 3 is a schematic flow chart of a method for solving brightness inconsistency between channels according to a preferred embodiment of the present invention.
  • FIGS. 1 to 3 specific embodiments. It should be noted that the drawings are in a very simplified form, using a non-precise ratio, and are only used to facilitate the purpose of the present embodiment.
  • a method for solving the brightness inconsistency between channels includes the following steps:
  • Step 01 providing two images, at a seam of the two images, setting a width range; the seam is surrounded by the set width;
  • FIG. 1 there is an image in which the channels are inconsistent, and a certain width is selected at the joint of the two images. As shown in FIG. 1, there is a seam between the gray image and the white image.
  • the width set here is i_weight.
  • Step 02 Calculate the brightness of each line of pixels in the set width range
  • the brightness of each row of pixels is the sum of each pixel value in the corresponding row divided by the set width.
  • Step 03 taking the coordinates of the pixel values of each row in the set width range as the abscissa and the ordinate, thereby obtaining a point set;
  • a pixel I (i, j) has an abscissa of i, sitting vertically Marked as j, thus forming a point coordinate, and then obtaining a set of points within the set width range.
  • Step 04 According to the method of obtaining the optimal solution in the Ransac algorithm, the point set is fitted to a straight line to obtain an optimal solution model of the point set;
  • the brightness of each row of pixels on the left side of the patch is equal to the brightness of the pixel of the corresponding row on the right side of the patch.
  • This step 04 specifically includes:
  • Step 041 Set a model with a minimum sampling set of n and a sample set P.
  • the sample set P is a set of points with the pixel values in each row as the horizontal and vertical coordinates, and the number of samples in the sample set P is greater than n; here, n is the minimum number of samples required to initialize the parameters of the model M, such as randomly taking 100 numbers, and then arbitrarily taking 2 different numbers among the 100 numbers; the sample set P is the horizontal and vertical values of the pixel values around the patchwork.
  • the set of points of the coordinates, the number of samples of the sample set P is greater than n, n ⁇ 1.
  • Step 042 Randomly extract a subset S of the sample set P containing n samples from the sample set P, and simulate to obtain an initialization model M i ; here, a subset of the sample set P containing n samples is randomly extracted from the sample set P
  • the S simulation results in an initialization model M i , which is generally a least squares method.
  • Step 043 The sample set of the remaining set of the sample set P and the initial model M i is smaller than the set threshold t, and the subset S constitutes a new set S*, and the set S* is an inner point set;
  • the sample set with the shortest distance from the initialization model M being less than a certain set threshold t and the subset S constitute a new set S*, the new set S* is considered to be the inner point set, and the number of samples in the set (S*) N i , where i is a positive integer starting from 1.
  • the corresponding initialization model (M i ) is the optimal solution model. Specifically, by setting the number of extractions in advance, the inner point set (S*) of the maximum number of samples is obtained, and the corresponding initial model (M i ) is required to obtain an optimal solution model, and the number of cycles is determined according to the required precision, preferably.
  • the number of cycles is 1000 to 2000 times.
  • Step 05 Find a fitting value of each pixel in one side of the seam in the optimal solution model, and adjust the brightness of the pixel at the corresponding position on the side of the seam according to the fitting value.

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  • Engineering & Computer Science (AREA)
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Abstract

一种解决通道间亮度不一致的方法,包括以下步骤:提供两个图像,在两个图像的拼缝处,设定一宽度范围;拼缝被所设定的宽度包围(01);计算出所设定的宽度范围内每行像素的亮度(02);以所设定的宽度范围内的每行像素值的坐标作为横坐标和纵坐标,从而得到点集合(03);根据Ransac算法中求取最优解的方法,将点集合拟合出一条直线,得到点集合的最优解模型(04);在最优解模型中找出拼缝一侧中每个像素的拟合值,按照该拟合值来调整拼缝一侧相应位置的像素的亮度(05)。该方法能够自动完成通道间亮度差别的弥补,优化图像效果。

Description

一种解决通道间亮度不一致的方法
本申请要求于2016年12月27日提交中国专利局、申请号为201611224484.3、名称为“一种解决通道间亮度不一致的方法”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
本发明涉及图像传感器技术领域,尤其涉及一种解决通道间亮度不一致的方法。
技术背景
在图像成像拼接过程中,出现了图像亮度不一致的问题,并且在图像的中间会有明显的缝隙,会使得图像看上去有明显的拼接缝隙,常规的使用方法是:将一个通道加一个值,使得两个通道间亮度一致,或者两个通道间调整增益,使一个通道乘以一个值得到相同的亮度,或者是在拼接缝隙处羽化处理,使得看上去平滑的过度,亮度变化看上去不明显。
但是前两种方法主要是针对两通道亮度变化一致的情况,面对通道间亮度变化不一致,也就是说可能上半部分和下半部分变化不同的情况下效果不明显,而最后一种羽化会影响图像的清晰度,面对高清晰度的情况不可行。
发明概要
本发明的目的在于弥补上述现有技术的不足,本发明旨在提供一种解决通道间亮度不一致的方法,能够自动识别通道间数值变化的最优解,改善图 像效果。
为了达到上述目的,本发明提供了一种解决通道间亮度不一致的方法,其包括以下步骤:
步骤01:提供两个图像,在两个图像的拼缝处,设定一宽度范围;拼缝被所设定的宽度包围;
步骤02:计算出所设定的宽度范围内每行像素的亮度;
步骤03:以所设定的宽度范围内的每行像素值的坐标作为横坐标和纵坐标,从而得到点集合;
步骤04:根据Ransac算法中求取最优解的方法,将点集合拟合出一条直线,得到点集合的最优解模型;
步骤05:在最优解模型中找出拼缝一侧中每个像素的拟合值,按照该拟合值来调整拼缝一侧相应位置的像素的亮度。
优选地,所述步骤02中,每行像素的亮度为相应行中每个像素值的总和除以所设定的宽度。
优选地,所述最优解模型中,拼缝左边的每行像素的亮度等于拼缝右边的相应行的像素的亮度。
优选地,所述步骤04具体包括:
步骤041:设定一个最小抽样集为n的模型和一个样本集P,样本集P为以每行中的像素值作为横纵坐标的点集合,样本集P中样本个数大于n;n为初始化模型Mi参数所需的最小样本数,n≥1;
步骤042:从样品集P中随机抽取包含n个样本的样本集P的子集S,模拟得到初始化模型Mi
步骤043:将样本集(P)的余集中与初始化模型(Mi)的最短距离小于设定阈值t的样本集,和子集(S)构成新的集合(S*),集合(S*)中样本的个数=Ni;其中,i为从1开始的正整数;
步骤044:当i=1时,将N1赋值给Nmax;当i≥1时,比较Ni与Nmax,若Ni≥Nmax,则认为Ni所对应的集合(S*)所对应的初始化模型(Mi)正确,令Nmax=Ni;若Ni≤Nmax,则保持Nmax不变;
步骤044:按照预设的循环次数重复步骤042至步骤044,每循环一次令i=i+1;从而得到样本的个数最大的集合(S*),并且样本的个数最大的集合(S*)所对应的初始化模型(Mi)为最优解模型。
优选地,所述步骤042中,采用最小二乘法得到初始化模型(Mi)。
优选地,所述步骤05具体包括:将拼缝左侧或右侧的所有像素与最优解模型中的相应像素对应起来,从而完成亮度调整。
本发明的解决通道间亮度不一致的方法,能够自动完成通道间亮度差别的弥补,优化图像效果。
附图说明
为能更清楚理解本发明的目的、特点和优点,以下将结合附图对本发明的较佳实施例进行详细描述,其中:
图1为本发明的一个较佳实施例的通道不一致的图像示意图
图2为本发明的一个较佳实施例的最优解模型示意图
图3为本发明的一个较佳实施例的解决通道间亮度不一致的方法的流程示意图。
发明内容
为使本发明的内容更加清楚易懂,以下结合说明书附图,对本发明的内容作进一步说明。当然本发明并不局限于该具体实施例,本领域内的技术人员所熟知的一般替换也涵盖在本发明的保护范围内。
以下结合附图1~3和具体实施例对本发明作进一步详细说明。需说明的是,附图均采用非常简化的形式、使用非精准的比例,且仅用以方便、清晰地达到辅助说明本实施例的目的。
请参阅图3,本实施例中,一种解决通道间亮度不一致的方法,包括以下步骤:
步骤01:提供两个图像,在两个图像的拼缝处,设定一宽度范围;拼缝被所设定的宽度包围;
具体的,如图1所示,存在通道不一致的图像,在两个图像的拼缝处,选取一定的宽度,如图1所示,灰色图像和白色图像之间存在拼缝。这里设定的宽度为i_weight。
步骤02:计算出所设定的宽度范围内每行像素的亮度;
具体的,每行像素的亮度为相应行中每个像素值的总和除以所设定的宽度。请继续参阅图1,第j行拼缝左边的亮度为Mean_left(1,j)=(I(i,j)+I(i-1,j)+…+I(i-i_weight+1,j))/i_weight,同理可以计算第j行拼缝右边的亮度Mean_right(1,j)。
步骤03:以所设定的宽度范围内的每行像素值的坐标作为横坐标和纵坐标,从而得到点集合;
具体的,请继续参阅图1,例如一个像素I(i,j)的横坐标为i,纵坐 标为j,从而构成一个点坐标,进而得到设定的宽度范围内的点集合。
步骤04:根据Ransac算法中求取最优解的方法,将点集合拟合出一条直线,得到点集合的最优解模型;
具体的,请参阅图2,最优解模型中,拼缝左边的每行像素的亮度等于拼缝右边的相应行的像素的亮度。
本步骤04具体包括:
步骤041:设定一个最小抽样集为n的模型和一个样本集P,样本集P为以每行中的像素值作为横纵坐标的点集合,样本集P中样本个数大于n;这里,n为初始化模型M的参数所需的最小样本数,比如随机取100个数,然后在100个数中任意取2个不相同的数;样本集P为以拼缝左右的像素值做横纵坐标的点集合,样本集P的样本个数大于n,n≥1。
步骤042:从样品集P中随机抽取包含n个样本的样本集P的子集S,模拟得到初始化模型Mi;这里,从样本集P中随机抽取包含n个样本的样本集P的子集S模拟得到初始化模型Mi,一般采用最小二乘法。
步骤043:将样品集P的余集中与初始化模型Mi的最短距离小于设定阈值t的样本集,和子集S构成新的集合S*,集合S*是内点集;具体的,余集中与初始化模型M的最短距离小于某一设定阈值t的样本集以及子集S构成新的集合S*,新的集合S*认为是内点集,集合(S*)中样本的个数=Ni,其中,i为从1开始的正整数。
步骤044:当i=1时,将N1赋值给Nmax,也即是令Nmax=Ni。当i≥1时,比较Ni与Nmax,若Ni≥Nmax,则认为Ni所对应的集合(S*)所对应的初始化模型(Mi)正确,令Nmax=Ni;若Ni≤Nmax,则认为Nmax所对应的集合(S*)所对 应的初始化模型(Mi)正确,则保持Nmax不变。
步骤045:按照预设的循环次数重复步骤042至步骤044,每循环一次令i=i+1,从而得到样本的个数最大的集合(S*),并且样本的个数最大的集合(S*)所对应的初始化模型(Mi)为最优解模型。具体的,通过预先设定抽取次数,得到最大的样本个数的内点集(S*),对应的初始化模型(Mi)所需最优解模型,循环次数根据所需要精度确定,较佳的,循环次数为1000~2000次。
步骤05:在最优解模型中找出拼缝一侧中每个像素的拟合值,按照该拟合值来调整拼缝一侧相应位置的像素的亮度。
具体的,由于拼缝左侧的像素亮度等于拼缝右侧的像素亮度,Mean_left=f(Mean_right),可以将拼缝左侧或右侧的所有像素与最优解模型中的相应像素对应起来,从而完成亮度调整。
虽然本发明已以较佳实施例揭示如上,然所述实施例仅为了便于说明而举例而已,并非用以限定本发明,本领域的技术人员在不脱离本发明精神和范围的前提下可作若干的更动与润饰,本发明所主张的保护范围应以权利要求书所述为准。

Claims (6)

  1. 一种解决通道间亮度不一致的方法,其特征在于,包括以下步骤:
    步骤01:提供两个图像,在两个图像的拼缝处,设定一宽度范围;拼缝被所设定的宽度包围;
    步骤02:计算出所设定的宽度范围内每行像素的亮度;
    步骤03:以所设定的宽度范围内的每行像素值的坐标作为横坐标和纵坐标,从而得到点集合;
    步骤04:根据Ransac算法中求取最优解的方法,将点集合拟合出一条直线,得到点集合的最优解模型;
    步骤05:在最优解模型中找出拼缝一侧中每个像素的拟合值,按照该拟合值来调整拼缝一侧相应位置的像素的亮度。
  2. 根据权利要求1所述的方法,其特征在于,所述步骤02中,每行像素的亮度为相应行中每个像素值的总和除以所设定的宽度。
  3. 根据权利要求1所述的方法,其特征在于,所述最优解模型中,拼缝左边的每行像素的亮度等于拼缝右边的相应行的像素的亮度。
  4. 根据权利要求1所述的方法,其特征在于,所述步骤04具体包括:
    步骤041:设定一个最小抽样集为n的模型和一个样本集(P),样本集(P)为以每行中的像素值作为横纵坐标的点集合,样本集(P)中样本个数大于n;n为初始化模型(Mi)参数所需的最小样本数,n≥1;
    步骤042:从样本集(P)中随机抽取包含n个样本的样本集P的子集(S),模拟得到初始化模型(Mi);
    步骤043:将样本集(P)的余集中与初始化模型(Mi)的最短距离小于设定阈值t的样本集,和子集(S)构成新的集合(S*),集合(S*)中样本的个数=Ni;其中,i为从1开始的正整数;
    步骤044:当i=1时,将N1赋值给Nmax;当i≥1时,比较Ni与Nmax,若Ni≥Nmax,则认为Ni所对应的集合(S*)所对应的初始化模型(Mi)正确,令Nmax=Ni;若Ni≤Nmax,则保持Nmax不变;
    步骤045:按照预设的循环次数重复步骤042至步骤044,每循环一次,令i=i+1,从而得到样本个数最大的集合(S*),并且样本个数最大的集合(S*)所对应的初始化模型(Mi)为最优解模型。
  5. 根据权利要求4所述的方法,其特征在于,所述步骤042中,采用最小二乘法得到初始化模型(Mi)。
  6. 根据权利要求1所述的方法,其特征在于,所述步骤05具体包括:将拼缝左侧或右侧的所有像素与最优解模型中的相应像素对应起来,从而完成亮度调整。
PCT/CN2017/115207 2016-12-27 2017-12-08 一种解决通道间亮度不一致的方法 WO2018121221A1 (zh)

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