WO2017049703A1 - 图像对比度增强方法 - Google Patents

图像对比度增强方法 Download PDF

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WO2017049703A1
WO2017049703A1 PCT/CN2015/092795 CN2015092795W WO2017049703A1 WO 2017049703 A1 WO2017049703 A1 WO 2017049703A1 CN 2015092795 W CN2015092795 W CN 2015092795W WO 2017049703 A1 WO2017049703 A1 WO 2017049703A1
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grayscale
pixels
gray
value
image
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French (fr)
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温亦谦
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TCL China Star Optoelectronics Technology Co Ltd
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Shenzhen China Star Optoelectronics Technology Co Ltd
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Priority to KR1020187005877A priority patent/KR102022812B1/ko
Priority to US14/888,452 priority patent/US9734564B2/en
Priority to JP2018513461A priority patent/JP6546696B2/ja
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/90—Dynamic range modification of images or parts thereof
    • G06T5/92—Dynamic range modification of images or parts thereof based on global image properties
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/40—Image enhancement or restoration using histogram techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/77—Retouching; Inpainting; Scratch removal
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/40—Picture signal circuits
    • H04N1/407—Control or modification of tonal gradation or of extreme levels, e.g. background level
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/40—Picture signal circuits
    • H04N1/409—Edge or detail enhancement; Noise or error suppression
    • H04N1/4092—Edge or detail enhancement
    • 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
    • G06T2207/10024—Color image
    • 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
    • G06T2207/20172—Image enhancement details
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/40—Picture signal circuits
    • H04N1/403—Discrimination between the two tones in the picture signal of a two-tone original
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/40—Picture signal circuits
    • H04N1/405—Halftoning, i.e. converting the picture signal of a continuous-tone original into a corresponding signal showing only two levels
    • H04N1/4051—Halftoning, i.e. converting the picture signal of a continuous-tone original into a corresponding signal showing only two levels producing a dispersed dots halftone pattern, the dots having substantially the same size
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/40—Picture signal circuits
    • H04N1/405—Halftoning, i.e. converting the picture signal of a continuous-tone original into a corresponding signal showing only two levels
    • H04N1/4051—Halftoning, i.e. converting the picture signal of a continuous-tone original into a corresponding signal showing only two levels producing a dispersed dots halftone pattern, the dots having substantially the same size
    • H04N1/4052—Halftoning, i.e. converting the picture signal of a continuous-tone original into a corresponding signal showing only two levels producing a dispersed dots halftone pattern, the dots having substantially the same size by error diffusion, i.e. transferring the binarising error to neighbouring dot decisions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/41—Bandwidth or redundancy reduction
    • H04N1/4105—Bandwidth or redundancy reduction for halftone screened pictures

Definitions

  • the present invention relates to the field of display technologies, and in particular, to an image contrast enhancement method.
  • Image enhancement technology is a kind of image processing technology, which can significantly improve the image quality, make the image content more layered and the subjective observation effect is more in line with people's needs.
  • the original image often has various problems, such as: the aperture is small when photographing, resulting in a dark image; the contrast of the scene is low, so that the focus of the image is not prominent; the overexposure causes the image to be abnormal, and the photo is whitened.
  • Image enhancement technology can effectively solve the above problems and improve display quality.
  • Contrast enhancement is to increase the distribution of grayscale of the image by adjusting the grayscale distribution of the image to improve the contrast of the whole or part of the image and improve the visual effect. Contrast enhancement is further divided into: Histogram Equalization and Gamma Correction.
  • the gamma correction method uses the gamma function as a mapping function to improve image contrast. This method is difficult to set when used for contrast enhancement. A gamma value suitable for each image, and the original color may change when the wrong gamma value is set.
  • the histogram equalization method is to obtain a higher contrast by compressing the gray scale with a smaller number of pixels and expanding the gray scale with a larger number of pixels.
  • the histogram equalization method is further divided into: Global Histogram Equalization (GHE) and Local Histogram Equalization (LHE).
  • GHE Global Histogram Equalization
  • LHE Local Histogram Equalization
  • the global histogram equalization mainly achieves the purpose of contrast enhancement by modifying the image histogram distribution;
  • Local histogram equalization is the effect of pre-defining a local contrast and then enhancing the local contrast to enhance image detail.
  • Figure 1 and Figure 2 show the histogram and display effect of the original image. It can be seen that the contrast of the original image is very low and the display effect is poor.
  • Contrast enhancement of an image using the existing global histogram equalization method typically includes the following steps:
  • Step 1 Convert the image to a grayscale map.
  • the conversion formula is:
  • Gray(i,j) is the grayscale value of one pixel
  • R(i,j), G(i,j), and B(i,j) are the red, green, and blue sub-pixels of the pixel, respectively.
  • Step 2 as shown in FIG. 3, the number of pixels corresponding to each grayscale value is counted according to the grayscale value from 0 to 255, and a histogram is generated correspondingly;
  • Step 3 As shown in FIG. 4, the gray scale value is calculated from the 0 to 255 for the number of pixels corresponding to each gray scale value, and the calculation formula is:
  • H(j) represents the number of pixels corresponding to the grayscale value j
  • Step 4 As shown in FIG. 5, the maximum value of the histogram accumulation is normalized, and the calculation formula is:
  • Step 5 Obtain a corresponding new grayscale value by looking up the table according to out(x).
  • FIG. 6 and FIG. 7 are respectively a histogram and a display effect diagram of the image after the contrast enhancement is performed by the above-mentioned conventional global histogram equalization method, and it can be seen that the contrast of the image after the contrast enhancement is improved to a certain extent. The display is improved, but the contrast is still low and the displayed image is distorted.
  • the present invention provides an image contrast enhancement method comprising the following steps:
  • Step 1 providing an image composed of a plurality of pixels arranged in a matrix, and converting the image into a grayscale map
  • Step 2 calculating the absolute value Q1 of the difference between the grayscale values of the adjacent two rows of pixels in the same column and the first grayscale value weight k1;
  • the calculation formula of the first gray scale value weight k1 is:
  • the absolute value Q1 of the difference of the grayscale values of the pixels of the adjacent two rows of the same column is 0.
  • n is a positive integer greater than one;
  • the cumulative calculation is performed according to the first gray scale value weight k1 and the gray scale values of two adjacent rows of pixels in the same column, and the calculation formula is:
  • Gray(i,j) is the grayscale value of the pixel in the i-th row and the jth column
  • Gray(i+1,j) is the i-th +1 row grayscale value of the jth column pixel
  • H1(a) is the number of pixels with a grayscale value a
  • C1(X) is the grayscale value Gray(i,j) to the grayscale value Gray(i+1) , j) the sum of the number of pixels corresponding to each grayscale value
  • Step 3 calculating the absolute value Q2 of the difference between the grayscale values of the adjacent two columns of pixels in the same row and the second grayscale value weight k2;
  • the formula for calculating the weight of the second grayscale value is:
  • the absolute value Q2 of the difference between the grayscale values of the pixels in the adjacent row of the same row ranges from 0 to 255, where n is a positive integer greater than 1 and is the same as the value in step 2;
  • Gray(i,j) is the grayscale value of the pixel in the i-th row and the j-th column
  • Gray(i,j+1) is the i-th
  • H3(a) is the number of pixels whose grayscale value is a
  • C3(X) is the grayscale value Gray(i,j) to the grayscale value Gray(i,j) +1) the sum of the number of pixels corresponding to each grayscale value
  • Step 4 adding C1 (X) in step 2 and C3 (X) in step 3 to obtain C (X);
  • Step 5 The maximum value is normalized, and the calculation formula is:
  • Each of the pixels includes: red, green, and blue sub-pixels.
  • R(i, j), G(i, j), and B(i, j) are the grayscale values corresponding to the red, green, and blue sub-pixels of the pixel of the i-th row and the jth column, respectively.
  • the image in the step 1 is an image displayed by the flat display device.
  • n 2, 3, or 4.
  • the first gray scale value weight k1 is inversely proportional to the nth root of the absolute value Q1 of the difference between the gray scale values of the adjacent two rows of pixels of the same column, and the second gray scale value weight k2 and the adjacent two columns of pixels of the same row
  • the absolute value of the difference of the gray scale values is inversely proportional to the nth root of the Q2.
  • the invention also provides an image contrast enhancement method, comprising the following steps:
  • Step 1 providing an image composed of a plurality of pixels arranged in a matrix, and converting the image into a grayscale map
  • Step 2 calculating the absolute value Q1 of the difference between the grayscale values of the adjacent two rows of pixels in the same column and the first grayscale value weight k1;
  • the calculation formula of the first gray scale value weight k1 is:
  • the absolute value Q1 of the difference of the grayscale values of the pixels of the adjacent two rows of the same column ranges from 0 to 255, and n is a positive integer greater than 1.
  • the cumulative calculation is performed according to the first gray scale value weight k1 and the gray scale values of two adjacent rows of pixels in the same column, and the calculation formula is:
  • Gray(i,j) is the grayscale value of the pixel in the i-th row and the jth column
  • Gray(i+1,j) is the i-th +1 row grayscale value of the jth column pixel
  • H1(a) is the number of pixels with a grayscale value a
  • C1(X) is the grayscale value Gray(i,j) to the grayscale value Gray(i+1) , j) the sum of the number of pixels corresponding to each grayscale value
  • Step 3 Calculate the absolute value Q2 and the second difference of the gray scale values of the adjacent two columns of pixels in the same row Gray scale value weight k2;
  • the absolute value Q2 of the difference between the grayscale values of the pixels in the adjacent row of the same row ranges from 0 to 255, where n is a positive integer greater than 1 and is the same as the value in step 2;
  • Gray(i,j) is the grayscale value of the pixel in the i-th row and the j-th column
  • Gray(i,j+1) is the i-th
  • H3(a) is the number of pixels whose grayscale value is a
  • C3(X) is the grayscale value Gray(i,j) to the grayscale value Gray(i,j) +1) the sum of the number of pixels corresponding to each grayscale value
  • Step 4 adding C1 (X) in step 2 and C3 (x) in step 3 to obtain C (X);
  • Step 5 The maximum value is normalized, and the calculation formula is:
  • each of the pixels comprises: red, green, and blue sub-pixels
  • the image in the step 1 is an image displayed by the flat display device
  • n 2, 3, or 4.
  • An image contrast enhancement method provided by the present invention calculates the absolute value of the gray scale difference between pixels adjacent to two rows of the same column and pixels adjacent to the same row, respectively, according to the absolute value Calculating the first and second grayscale value weights, and then performing the cumulative calculation and the normalization processing by using the first and second grayscale value weights, and finally obtaining the enhanced grayscale table, thereby reallocating the grayscale values of the respective pixels, Improves image contrast, reduces image distortion, and optimizes display.
  • Figure 1 is a histogram of the original image
  • step 2 is a schematic diagram of step 2 of contrast enhancement of an image using an existing global histogram equalization method
  • step 3 is a schematic diagram of step 3 of contrast enhancement of an image using an existing global histogram equalization method
  • step 4 is a schematic diagram of step 4 of contrast enhancement of an image using an existing global histogram equalization method
  • 6 is a histogram of an image after contrast enhancement of an image by an existing global histogram equalization method
  • FIG. 7 is a display effect diagram of an image after contrast enhancement of an image by an existing global histogram equalization method
  • step 4 of the image contrast enhancement method of the present invention is a schematic diagram of step 4 of the image contrast enhancement method of the present invention.
  • step 5 of the image contrast enhancement method of the present invention is a schematic diagram of step 5 of the image contrast enhancement method of the present invention.
  • Figure 11 is a histogram of an image after contrast enhancement of an image by the image contrast enhancement method of the present invention.
  • FIG. 12 is a diagram showing an effect of displaying an image after contrast enhancement of an image by the image contrast enhancement method of the present invention.
  • the present invention provides an image contrast enhancement method, including the following steps:
  • each of the pixels includes: red, green, and blue sub-pixels.
  • the image turns The conversion formula for the grayscale graph is:
  • R(i, j), G(i, j), and B(i, j) are the grayscale values corresponding to the red, green, and blue sub-pixels of the pixel of the i-th row and the jth column, respectively.
  • the image in the step 1 is an image displayed by a flat display device such as an LCD display, an OLED display or the like.
  • Step 2 calculating the absolute value Q1 of the difference between the grayscale values of the adjacent two rows of pixels in the same column and the first grayscale value weight k1;
  • the calculation formula of the first gray scale value weight k1 is:
  • the absolute value Q1 of the difference of the grayscale values of the pixels of the adjacent two rows of the same column ranges from 0 to 255, and n is a positive integer greater than 1. Further, the value of n is preferably 2, 3, or 4.
  • the calculation formula of the first gray scale value weight k1 is that the first gray scale value weight k1 is inversely proportional to the nth root of the absolute value Q1 of the difference between the gray scale values of the pixels of the adjacent two rows of the same column.
  • i and j are positive integers, respectively representing the number of rows and columns in which the pixel is located
  • Gray(i,j) is the grayscale value of the pixel in the i-th row and the jth column
  • Gray(i+1,j) is the i-th +1 row grayscale value of the jth column pixel
  • H1(a) is the number of pixels with a grayscale value a
  • C1(X) is the grayscale value Gray(i,j) to the grayscale value Gray(i+1) , j) the sum of the number of pixels corresponding to each grayscale value
  • X is a positive integer between 0 and 255.
  • Step 3 calculating the absolute value Q2 of the difference between the grayscale values of the adjacent two columns of pixels in the same row and the second grayscale value weight k2;
  • the absolute value Q2 of the difference between the grayscale values of the pixels in the adjacent row of the same row ranges from 0 to 255, where n is a positive integer greater than 1 and is the same as the value in step 2; further, n The value is preferably 2, 3, or 4.
  • the second gray scale value weight k2 is inversely proportional to the nth root of the absolute value Q2 of the difference between the gray scale values of the pixels of the adjacent two columns of the same row.
  • Gray(i,j) is the grayscale value of the pixel in the i-th row and the j-th column
  • Gray(i,j+1) is the i-th
  • H3(a) is the number of pixels whose grayscale value is a
  • C3(X) is the grayscale value Gray(i,j) to the grayscale value Gray(i,j) +1) the sum of the number of pixels corresponding to each grayscale value
  • X is a positive integer between 0 and 255.
  • Step 4 as shown in FIG. 9, adding C1(X) in step 2 to C3(X) in step 3 to obtain C(X),
  • Step 5 As shown in FIG. 10, the maximum value is normalized, and the calculation formula is:
  • the gray scale distribution of the image is more uniform, the contrast of the image is greatly improved compared with the prior art, the image distortion is reduced, and the display effect is improved. optimization.
  • the image contrast enhancement method of the present invention calculates the absolute value of the gray scale difference between the pixels of two adjacent rows of the same column and the adjacent two columns of the same row, and calculates the first and the first according to the absolute value.
  • the weight value of the two gray scale values is calculated by the first and second gray scale value weights, and finally the enhanced gray scale table is obtained, and the gray scale values of the respective pixels are redistributed, thereby improving the contrast of the image. , reduce image distortion and optimize display.

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Abstract

本发明提供一种图像对比度增强方法，该方法通过分别计算同一列相邻两行和同一行相邻两列的像素之间的灰阶差的绝对值，依据该绝对值分别计算第一、第二灰阶值权重，再通过第一、第二灰阶值权重进行累加计算和归一化处理，最终获得增强灰阶表，进而对各个像素的灰阶值进行重新分配，能够提高图像的对比度，减小图像失真，优化显示效果。

Description

图像对比度增强方法 技术领域
本发明涉及显示技术领域，尤其涉及一种图像对比度增强方法。
背景技术
图像增强技术是图像处理技术的一种，它可以显著改善图像质量，使得图像内容更有层次感并且主观观测效果更符合人们需求。现实生活中，原始图像往往存在各种问题，例如：拍照时光圈偏小，导致图像偏暗；场景的对比度较低，而使得图像重点不突出；曝光过度，导致影像失常，照片泛白等。通过图像增强技术可以有效解决上述问题，提升显示质量。
常见的图像增强技术包括：饱和度增强和对比度增强，相比于饱和度增强，对比度增强受到的关注度更高。对比度增强是通过调节图像的灰阶分布，增加图像灰阶的分布范围，以提高图像整体或部分的对比度，改善视觉效果。而对比度增强又分为：直方图均衡(Histogram Equalization)与伽马校正，其中伽马校正方法将伽马函数作为映射函数使用，从而提高图像对比度，该方法在用于对比度增强时，很难设置一个适合于每幅图像的伽马值，且当设置了错误的伽马值时，原始色彩可能会发生变化。直方图均衡方法是通过压缩像素数较少的灰阶并扩展像素数较多的灰阶，从而使得处理后图像获得较高的对比度。
直方图均衡方法又分为：全局直方图均衡(Global Histogram Equalization，GHE)及局部直方图均衡(Local Histogram Equalization，LHE)，全局直方图均衡主要通过修改图像直方图分布达到对比度增强的目的；而局部直方图均衡是预先定义一个局部对比度，然后增强该局部对比度达到增强图像细节的效果。
图1、图2所示分别为原始图像的直方图与显示效果图，能够看出原始图像的对比度非常低，显示效果差。
采用现有的全局直方图均衡方法对图像做对比度增强通常包括以下步骤：
步骤1、将图像转为灰阶图，转换公式为：
Gray(i,j)＝((R(i,j)+G(i,j)+B(i,j))/3
其中，Gray(i,j)为一像素的灰阶值，R(i,j)、G(i,j)、和B(i,j)分别为该像素的红色、绿色、及蓝色子像素对应的灰阶值；
步骤2、如图3所示，按照灰阶值从0到255统计每一灰阶值对应的像素数量，并相应制作直方图；
步骤3、如图4所示，将灰阶值从0到255对每一灰阶值对应的像素数量做直方图累加计算，计算公式为:
Figure PCTCN2015092795-appb-000001
其中，H(j)表示对应于灰阶值j的像素数量；
步骤4、如图5所示，将直方图累加的最大值归一化，计算公式为：
Figure PCTCN2015092795-appb-000002
再将归一化处理后的数据乘上255，得到：
out(x)＝N(x)×255；
步骤5、依据out(x)通过查表获得对应的新的灰阶值。
图6、图7所示分别为经上述现有的全局直方图均衡方法对图像做对比度增强后图像的直方图与显示效果图，能够看出对比度增强后图像的对比度得到了一定程度的提高，显示效果得以改善，但对比度仍较低，显示图像存在失真。
发明内容
本发明的目的在于提供一种图像对比度增强方法，能够提高图像的对比度，减小图像失真，优化显示效果。
为实现上述目的，本发明提供了一种图像对比度增强方法，包括如下步骤：
步骤1、提供一由呈矩阵式排布的多个像素组成的图像，并将该图像转换为灰阶图；
步骤2、计算每同一列相邻两行像素的灰阶值的差的绝对值Q1和第一灰阶值权重k1；
每同一列相邻两行像素的灰阶值的差的绝对值Q1的计算公式为：
Q1＝abs(Gray(i，j)－Gray(i+1，j))
第一灰阶值权重k1的计算公式为：
Figure PCTCN2015092795-appb-000003
其中，同一列相邻两行像素的灰阶值的差的绝对值Q1的取值范围为0 至255，n为大于1的正整数；
依据第一灰阶值权重k1与每同一列相邻两行像素的灰阶值进行累加计算，计算公式为：
Figure PCTCN2015092795-appb-000004
其中，i、j为正整数，分别代表像素所在的行数与列数，Gray(i,j)为第i行第j列像素的灰阶值，Gray(i+1,j)为第i+1行第j列像素的灰阶值，H1(a)为灰阶值为a的像素数量，C1(X)为从灰阶值Gray(i,j)到灰阶值Gray(i+1，j)之间各个灰阶值对应的像素数量之和；
步骤3、计算每同一行相邻两列像素的灰阶值的差的绝对值Q2和第二灰阶值权重k2；
每同一行相邻两列像素的灰阶值的差的绝对值Q2的计算公式为：
Q2＝abs(Gray(i，j)－Gray(i，j+1))
第二灰阶值权重的计算公式为：
Figure PCTCN2015092795-appb-000005
其中，同一行相邻两列像素的灰阶值的差的绝对值Q2的取值范围为0至255，n为大于1的正整数且与步骤2中的取值相同；
依据第二灰阶值权重k2与每同一行相邻两列像素的灰阶值进行累加计算，计算公式为：
Figure PCTCN2015092795-appb-000006
其中，i、j为正整数，分别代表像素所在的行数与列数，Gray(i,j)为第i行第j列像素的灰阶值，Gray(i,j+1)为第i行第j+1列像素的灰阶值，H3(a)为灰阶值为a的像素数量，C3(X)为从灰阶值Gray(i,j)到灰阶值Gray(i，j+1)之间各个灰阶值对应的像素数量之和；
步骤4、将步骤2中的C1(X)与步骤3中C3(X)相加得到C(X)；
C(X)＝C1(X)+C3(X)
步骤5、最大值归一化，计算公式为：
Figure PCTCN2015092795-appb-000007
再将N(X)乘以255计算得到增强灰阶表out(X)，并通过查表得到新的输出灰阶值out_gray(i,j)。
所述每一像素包括：红色、绿色、和蓝色子像素。
该图像转为灰阶图的转换公式为：
Gray(i,j)＝(R(i,j)+G(i,j)+B(i,j))/3
其中，R(i,j)、G(i,j)、和B(i,j)分别为第i行第j列像素的红色、绿色、及蓝色子像素对应的灰阶值。
X为0到255之间的正整数。
所述第一灰阶值权重k1与第二灰阶值权重k2相同或不同。
所述步骤1中的图像为平面显示设备显示的图像。
所述步骤2与步骤3中n为2、3、或4。
所述第一灰阶值权重k1和同一列相邻两行像素的灰阶值的差的绝对值Q1的n次方根成反比，所述第二灰阶值权重k2和同一行相邻两列像素的灰阶值的差的绝对值Q2的n次方根成反比。
本发明还提供一种图像对比度增强方法，包括如下步骤：
步骤1、提供一由呈矩阵式排布的多个像素组成的图像，并将该图像转换为灰阶图；
步骤2、计算每同一列相邻两行像素的灰阶值的差的绝对值Q1和第一灰阶值权重k1；
每同一列相邻两行像素的灰阶值的差的绝对值Q1的计算公式为：
Q1＝abs(Gray(i，j)－Gray(i+1，j))
第一灰阶值权重k1的计算公式为：
Figure PCTCN2015092795-appb-000008
其中，同一列相邻两行像素的灰阶值的差的绝对值Q1的取值范围为0至255，n为大于1的正整数；
依据第一灰阶值权重k1与每同一列相邻两行像素的灰阶值进行累加计算，计算公式为：
Figure PCTCN2015092795-appb-000009
其中，i、j为正整数，分别代表像素所在的行数与列数，Gray(i,j)为第i行第j列像素的灰阶值，Gray(i+1,j)为第i+1行第j列像素的灰阶值，H1(a)为灰阶值为a的像素数量，C1(X)为从灰阶值Gray(i,j)到灰阶值Gray(i+1，j)之间各个灰阶值对应的像素数量之和；
步骤3、计算每同一行相邻两列像素的灰阶值的差的绝对值Q2和第二 灰阶值权重k2；
每同一行相邻两列像素的灰阶值的差的绝对值Q2的计算公式为：
Q2＝abs(Gray(i，j)－Gray(i，j+1))
第二灰阶值权重k2的计算公式为：
Figure PCTCN2015092795-appb-000010
其中，同一行相邻两列像素的灰阶值的差的绝对值Q2的取值范围为0至255，n为大于1的正整数且与步骤2中的取值相同；
依据第二灰阶值权重k2与每同一行相邻两列像素的灰阶值进行累加计算，计算公式为：
Figure PCTCN2015092795-appb-000011
其中，i、j为正整数，分别代表像素所在的行数与列数，Gray(i,j)为第i行第j列像素的灰阶值，Gray(i,j+1)为第i行第j+1列像素的灰阶值，H3(a)为灰阶值为a的像素数量，C3(X)为从灰阶值Gray(i,j)到灰阶值Gray(i，j+1)之间各个灰阶值对应的像素数量之和；
步骤4、将步骤2中的C1(X)与步骤3中C3(x)相加得到C(X)；
C(X)＝C1(X)+C3(X)
步骤5、最大值归一化，计算公式为：
Figure PCTCN2015092795-appb-000012
再将N(X)乘以255计算得到增强灰阶表out(X)，并通过查表得到新的输出灰阶值out_gray(i,j)；
其中，所述每一像素包括：红色、绿色、和蓝色子像素；
其中，所述步骤1中的图像为平面显示设备显示的图像；
其中，所述步骤2与步骤3中n为2、3、或4。
本发明的有益效果：本发明提供的一种图像对比度增强方法，通过分别计算同一列相邻两行和同一行相邻两列的像素之间的灰阶差的绝对值，依据该绝对值分别计算第一、第二灰阶值权重，再通过第一、第二灰阶值权重进行累加计算和归一化处理，最终获得增强灰阶表，进而对各个像素的灰阶值进行重新分配，能够提高图像的对比度，减小图像失真，优化显示效果。
为了能更进一步了解本发明的特征以及技术内容，请参阅以下有关本 发明的详细说明与附图，然而附图仅提供参考与说明用，并非用来对本发明加以限制。
附图说明
下面结合附图，通过对本发明的具体实施方式详细描述，将使本发明的技术方案及其它有益效果显而易见。
附图中，
图1为原始图像的直方图；
图2为原始图像的显示效果图；
图3为采用现有的全局直方图均衡方法对图像做对比度增强的步骤2的示意图；
图4为采用现有的全局直方图均衡方法对图像做对比度增强的步骤3的示意图；
图5为采用现有的全局直方图均衡方法对图像做对比度增强的步骤4的示意图；
图6为经现有的全局直方图均衡方法对图像做对比度增强后图像的直方图；
图7为经现有的全局直方图均衡方法对图像做对比度增强后图像的显示效果图；
图8为本发明的图像对比度增强方法的流程图；
图9为本发明的图像对比度增强方法的步骤4的示意图；
图10为本发明的图像对比度增强方法的步骤5的示意图；
图11经本发明的图像对比度增强方法对图像做对比度增强后图像的直方图；
图12经本发明的图像对比度增强方法对图像做对比度增强后图像的显示效果图。
具体实施方式
为更进一步阐述本发明所采取的技术手段及其效果，以下结合本发明的优选实施例及其附图进行详细描述。
请参阅图8，本发明提供一种图像对比度增强方法，包括如下步骤：
步骤1、提供一由呈矩阵式排布的多个像素组成的图像，并将该图像转换为灰阶图。
具体地，所述每一像素包括：红色、绿色、和蓝色子像素。该图像转 为灰阶图的转换公式为：
Gray(i,j)＝((R(i,j)+G(i,j)+B(i,j))/3
其中，R(i,j)、G(i,j)、和B(i,j)分别为第i行第j列像素的红色、绿色、及蓝色子像素对应的灰阶值。
该步骤1中的图像为平面显示设备如LCD显示器、OLED显示器等显示的图像。
步骤2、计算每同一列相邻两行像素的灰阶值的差的绝对值Q1和第一灰阶值权重k1；
每同一列相邻两行像素的灰阶值的差的绝对值Q1的计算公式为：
Q1＝abs(Gray(i，j)－Gray(i+1，j))
第一灰阶值权重k1的计算公式为：
Figure PCTCN2015092795-appb-000013
其中，同一列相邻两行像素的灰阶值的差的绝对值Q1的取值范围为0至255，n为大于1的正整数，进一步地，n的取值优选为2、3、或4。
由第一灰阶值权重k1的计算公式可知所述第一灰阶值权重k1和同一列相邻两行像素的灰阶值的差的绝对值Q1的n次方根成反比，
依据第一灰阶值权重k1与每同一列相邻两行像素的灰阶值进行累加计算，计算公式为：
Figure PCTCN2015092795-appb-000014
其中，i、j为正整数，分别代表像素所在的行数与列数，Gray(i,j)为第i行第j列像素的灰阶值，Gray(i+1,j)为第i+1行第j列像素的灰阶值，H1(a)为灰阶值为a的像素数量，C1(X)为从灰阶值Gray(i,j)到灰阶值Gray(i+1，j)之间各个灰阶值对应的像素数量之和；X为0到255之间的正整数。
步骤3、计算每同一行相邻两列像素的灰阶值的差的绝对值Q2和第二灰阶值权重k2；
每同一行相邻两列像素的灰阶值的差的绝对值Q2的计算公式为：
Q2＝abs(Gray(i，j)－Gray(i，j+1))
第二灰阶值权重k2的计算公式为：
Figure PCTCN2015092795-appb-000015
其中，同一行相邻两列像素的灰阶值的差的绝对值Q2的取值范围为0至255，n为大于1的正整数且与步骤2中的取值相同，；进一步地，n的取值优选为2、3、或4。
由第二灰阶值权重k2的计算公式可知所述第二灰阶值权重k2和同一行相邻两列像素的灰阶值的差的绝对值Q2的n次方根成反比。
依据第二灰阶值权重k2与每同一行相邻两列像素的灰阶值进行累加计算，计算公式为：
Figure PCTCN2015092795-appb-000016
其中，i、j为正整数，分别代表像素所在的行数与列数，Gray(i,j)为第i行第j列像素的灰阶值，Gray(i,j+1)为第i行第j+1列像素的灰阶值，H3(a)为灰阶值为a的像素数量，C3(X)为从灰阶值Gray(i,j)到灰阶值Gray(i，j+1)之间各个灰阶值对应的像素数量之和；X为0到255之间的正整数。
步骤4、如图9所示，将步骤2中的C1(X)与步骤3中C3(X)相加得到C(X)，
即C(X)＝C1(X)+C3(X)。
步骤5、如图10所示，最大值归一化，计算公式为：
Figure PCTCN2015092795-appb-000017
再将N(X)乘以255计算得到增强灰阶表out(x)，并通过查表得到新的输出灰阶值out_gray(i,j)。
请同时参阅图11与图12，经本发明的图像对比度增强方法对图像做对比度增强后，图像的灰阶分布更均匀，图像的对比度较现有技术大幅提高，图像失真减小，显示效果得以优化。
综上所述，本发明的图像对比度增强方法通过分别计算同一列相邻两行和同一行相邻两列的像素之间的灰阶差的绝对值，依据该绝对值分别计算第一、第二灰阶值权重，再通过第一、第二灰阶值权重进行累加计算和归一化处理，最终获得增强灰阶表，进而对各个像素的灰阶值进行重新分配，能够提高图像的对比度，减小图像失真，优化显示效果。
以上所述，对于本领域的普通技术人员来说，可以根据本发明的技术方案和技术构思作出其他各种相应的改变和变形，而所有这些改变和变形都应属于本发明权利要求的保护范围。

Claims (13)

  1. 一种图像对比度增强方法，包括如下步骤：
    步骤1、提供一由呈矩阵式排布的多个像素组成的图像，并将该图像转换为灰阶图；
    步骤2、计算每同一列相邻两行像素的灰阶值的差的绝对值Q1和第一灰阶值权重k1；
    每同一列相邻两行像素的灰阶值的差的绝对值Q1的计算公式为：
    Q1＝abs(Gray(i，j)－Gray(i+1，j))
    第一灰阶值权重k1的计算公式为：
    Figure PCTCN2015092795-appb-100001
    其中，同一列相邻两行像素的灰阶值的差的绝对值Q1的取值范围为0至255，n为大于1的正整数；
    依据第一灰阶值权重k1与每同一列相邻两行像素的灰阶值进行累加计算，计算公式为：
    Figure PCTCN2015092795-appb-100002
    其中，i、j为正整数，分别代表像素所在的行数与列数，Gray(i,j)为第i行第j列像素的灰阶值，Gray(i+1,j)为第i+1行第j列像素的灰阶值，H1(a)为灰阶值为a的像素数量，C1(X)为从灰阶值Gray(i,j)到灰阶值Gray(i+1，j)之间各个灰阶值对应的像素数量之和；
    步骤3、计算每同一行相邻两列像素的灰阶值的差的绝对值Q2和第二灰阶值权重k2；
    每同一行相邻两列像素的灰阶值的差的绝对值Q2的计算公式为：
    Q2＝abs(Gray(i，j)－Gray(i，j+1))
    第二灰阶值权重k2的计算公式为：
    Figure PCTCN2015092795-appb-100003
    其中，同一行相邻两列像素的灰阶值的差的绝对值Q2的取值范围为0至255，n为大于1的正整数且与步骤2中的取值相同；
    依据第二灰阶值权重k2与每同一行相邻两列像素的灰阶值进行累加计算，计算公式为：
    Figure PCTCN2015092795-appb-100004
    其中，i、j为正整数，分别代表像素所在的行数与列数，Gray(i,j)为第i行第j列像素的灰阶值，Gray(i,j+1)为第i行第j+1列像素的灰阶值，H3(a)为灰阶值为a的像素数量，C3(X)为从灰阶值Gray(i,j)到灰阶值Gray(i，j+1)之间各个灰阶值对应的像素数量之和；
    步骤4、将步骤2中的C1(X)与步骤3中C3(x)相加得到C(X)；
    C(X)＝C1(X)+C3(X)
    步骤5、最大值归一化，计算公式为：
    Figure PCTCN2015092795-appb-100005
    再将N(X)乘以255计算得到增强灰阶表out(X)，并通过查表得到新的输出灰阶值out_gray(i,j)。
  2. 如权利要求1所述的图像对比度增强方法，其中，所述每一像素包括：红色、绿色、和蓝色子像素。
  3. 如权利要求2所述的图像对比度增强方法，其中，该图像转为灰阶图的转换公式为：
    Gray(i,j)＝(R(i,j)+G(i,j)+B(i,j))/3
    其中，R(i,j)、G(i,j)、和B(i,j)分别为第i行第j列像素的红色、绿色、及蓝色子像素对应的灰阶值。
  4. 如权利要求1所述的图像对比度增强方法，其中，X为0到255之间的正整数。
  5. 如权利要求1所述的图像对比度增强方法，其中，所述第一灰阶值权重k1与第二灰阶值权重k2相同或不同。
  6. 如权利要求1所述的图像对比度增强方法，其中，所述步骤1中的图像为平面显示设备显示的图像。
  7. 如权利要求1所述的图像对比度增强方法，其中，所述步骤2与步骤3中n为2、3、或4。
  8. 如权利要求1所述的图像对比度增强方法，其中，所述第一灰阶值权重k1和同一列相邻两行像素的灰阶值的差的绝对值Q1的n次方根成反比，所述第二灰阶值权重k2和同一行相邻两列像素的灰阶值的差的绝对值Q2的n次方根成反比。
  9. 一种图像对比度增强方法，包括如下步骤：
    步骤1、提供一由呈矩阵式排布的多个像素组成的图像，并将该图像转换为灰阶图；
    步骤2、计算每同一列相邻两行像素的灰阶值的差的绝对值Q1和第一灰阶值权重k1；
    每同一列相邻两行像素的灰阶值的差的绝对值Q1的计算公式为：
    Q1＝abs(Gray(i，j)－Gray(i+1，j))
    第一灰阶值权重k1的计算公式为：
    Figure PCTCN2015092795-appb-100006
    其中，同一列相邻两行像素的灰阶值的差的绝对值Q1的取值范围为0至255，n为大于1的正整数；
    依据第一灰阶值权重k1与每同一列相邻两行像素的灰阶值进行累加计算，计算公式为：
    Figure PCTCN2015092795-appb-100007
    其中，i、j为正整数，分别代表像素所在的行数与列数，Gray(i,j)为第i行第j列像素的灰阶值，Gray(i+1,j)为第i+1行第j列像素的灰阶值，H1(a)为灰阶值为a的像素数量，C1(X)为从灰阶值Gray(i,j)到灰阶值Gray(i+1，j)之间各个灰阶值对应的像素数量之和；
    步骤3、计算每同一行相邻两列像素的灰阶值的差的绝对值Q2和第二灰阶值权重k2；
    每同一行相邻两列像素的灰阶值的差的绝对值Q2的计算公式为：
    Q2＝abs(Gray(i，j)－Gray(i，j+1))
    第二灰阶值权重k2的计算公式为：
    Figure PCTCN2015092795-appb-100008
    其中，同一行相邻两列像素的灰阶值的差的绝对值Q2的取值范围为0至255，n为大于1的正整数且与步骤2中的取值相同；
    依据第二灰阶值权重k2与每同一行相邻两列像素的灰阶值进行累加计算，计算公式为：
    Figure PCTCN2015092795-appb-100009
    其中，i、j为正整数，分别代表像素所在的行数与列数，Gray(i,j)为第i行第j列像素的灰阶值，Gray(i,j+1)为第i行第j+1列像素的灰阶值，H3(a)为灰阶值为a的像素数量，C3(X)为从灰阶值Gray(i,j)到灰阶值Gray(i，j+1)之间各个灰阶值对应的像素数量之和；
    步骤4、将步骤2中的C1(X)与步骤3中C3(x)相加得到C(X)；
    C(X)＝C1(X)+C3(X)
    步骤5、最大值归一化，计算公式为：
    Figure PCTCN2015092795-appb-100010
    再将N(X)乘以255计算得到增强灰阶表out(X)，并通过查表得到新的输出灰阶值out_gray(i,j)；
    其中，所述每一像素包括：红色、绿色、和蓝色子像素；
    其中，所述步骤1中的图像为平面显示设备显示的图像；
    其中，所述步骤2与步骤3中n为2、3、或4。
  10. 如权利要求9所述的图像对比度增强方法，其中，该图像转为灰阶图的转换公式为：
    Gray(i,j)＝(R(i,j)+G(i,j)+B(i,j))/3
    其中，R(i,j)、G(i,j)、和B(i,j)分别为第i行第j列像素的红色、绿色、及蓝色子像素对应的灰阶值。
  11. 如权利要求9所述的图像对比度增强方法，其中，X为0到255之间的正整数。
  12. 如权利要求9所述的图像对比度增强方法，其中，所述第一灰阶值权重k1与第二灰阶值权重k2相同或不同。
  13. 如权利要求9所述的图像对比度增强方法，其中，所述第一灰阶值权重k1和同一列相邻两行像素的灰阶值的差的绝对值Q1的n次方根成反比，所述第二灰阶值权重k2和同一行相邻两列像素的灰阶值的差的绝对值Q2的n次方根成反比。
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