WO2020107646A1 - 图像处理方法 - Google Patents

图像处理方法 Download PDF

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WO2020107646A1
WO2020107646A1 PCT/CN2019/070311 CN2019070311W WO2020107646A1 WO 2020107646 A1 WO2020107646 A1 WO 2020107646A1 CN 2019070311 W CN2019070311 W CN 2019070311W WO 2020107646 A1 WO2020107646 A1 WO 2020107646A1
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virtual
image
exposure
brightness
blocks
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PCT/CN2019/070311
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English (en)
French (fr)
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赖庆鸿
金羽锋
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深圳市华星光电半导体显示技术有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4038—Image mosaicing, e.g. composing plane images from plane sub-images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06T2207/20208—High dynamic range [HDR] image processing

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  • the present invention relates to the field of display technology, and in particular, to an image processing method.
  • the sensors available in digital imaging devices such as cameras generally capture a much smaller brightness range.
  • Traditional digital imaging equipment shoots an image of a scene with a single exposure, so the image contains only a limited range of brightness contrast. Specifically, during a longer exposure time, the exposure is higher. At this time, although the scene is shot The low-brightness area can obtain clearer image details, but the high-brightness area will exhibit overexposure.
  • HDR imaging technology has gradually become an increasingly popular imaging technology in digital imaging devices.
  • the image obtained by HDR imaging is also referred to as an HDR image, and such an HDR image can provide a high brightness range from a darker area to a completely illuminated area in the scene.
  • An existing image processing method obtains multiple images with different exposure levels, and performs weight fusion processing on multiple images with different exposure levels to finally obtain an HDR image with a high brightness range.
  • This method uses multiple exposure images For fusion, the image displacement problem is not easy to deal with.
  • Another existing image processing method generates multiple virtual exposure images by using a single input image, and then performs weight fusion processing on the multiple virtual exposure images to finally obtain an HDR image with a high brightness range.
  • the weighted image of the virtual exposure image is processed with a large amount of calculation, and contrast enhancement cannot be performed for individual brightness blocks.
  • An object of the present invention is to provide an image processing method that can make an image have better image details, improve the quality of the image, and can increase the speed of image processing.
  • the present invention provides an image processing method, including the following steps:
  • Step S1 providing the original image
  • Step S2 Perform a brightness clustering partition on the original image, and divide the original image into L brightness blocks, and the brightness of the L brightness blocks is different, where L is a positive integer;
  • Step S3 using the original image to generate L virtual exposure images, the exposure degrees of the L virtual exposure images are different; each virtual exposure image has L virtual blocks respectively corresponding to the L brightness blocks of the original image, The position of each virtual block in the virtual exposure image where it is located is the same as the position of the corresponding brightness block in the original image;
  • Step S4 Select one of the L virtual blocks of each virtual exposure image as the virtual block to be fused, and set i to a positive integer greater than 0 and less than or equal to L.
  • the L The virtual blocks to be merged of the i-th virtual exposure image in the virtual exposure images are the i-th virtual blocks when the L virtual blocks of the i-th virtual exposure image are sorted according to the brightness from small to large;
  • Step S5 Splicing multiple virtual blocks to be merged to obtain a processed image, so that the position of each virtual block to be merged in the processed image is the same as the position of the corresponding brightness block in the original image.
  • a clustering algorithm is used to perform a luminance clustering partition on the original image.
  • the original image is clustered by brightness clustering by means of K-means clustering.
  • the preset image is used to adjust the exposure value of the original image to generate L virtual exposure images.
  • the adjustment formula is:
  • L wk (x, y) is the brightness value at the (x, y) pixel in the kth virtual exposure image of the generated L virtual exposure images
  • L d ( x, y) is the normalized brightness value at the (x, y) pixel in the original image
  • P k is the brightness adjustment factor of the k-th virtual exposure image among the L virtual exposure images
  • L ad, k is L
  • L smax is a preset fixed value
  • L max, k is the maximum brightness value in the kth virtual exposure image among the L virtual exposure images
  • EV k is the exposure value of the k-th virtual exposure image among the L virtual exposure images
  • ⁇ is a preset constant
  • step S5 when a plurality of virtual blocks to be merged are spliced, the splicing parts of two adjacent blocks to be merged are smoothed.
  • Linear interpolation is used to smooth the spliced parts of two adjacent blocks to be merged.
  • the histogram equalization algorithm is used to enhance the contrast of multiple virtual blocks to be merged.
  • the original image is divided into brightness clusters, and the original image is used to generate a plurality of virtual exposure images with different exposure degrees, and each virtual exposure image is divided according to the brightness cluster partition of the original image
  • the virtual blocks to be merged are selected from the multiple virtual blocks of each virtual exposure image, and the multiple virtual blocks to be merged are spliced to obtain the processed image, which can make the image have better Image details, improve the quality of the image, and can increase the speed of image processing.
  • step S2 is a schematic diagram of step S2 of an embodiment of the image processing method of the present invention.
  • step S3 is a schematic diagram of step S3 according to an embodiment of the image processing method of the present invention.
  • step S4 is a schematic diagram of step S4 and step S5 of an embodiment of the image processing method of the present invention.
  • the present invention provides an image processing method, including the following steps:
  • Step S1 please refer to FIG. 2 to provide an original image 10.
  • Step S2 perform a brightness clustering partition on the original image 10 to divide the original image 10 into L brightness blocks, and the brightness of the L brightness blocks is different, where L is a positive integer.
  • L 3, that is, in the step S2, after the original image 10 is subjected to a luminance clustering partition, the original image 10 is divided into 3 luminance blocks , Respectively, are the first brightness block 11, the second brightness block 12, and the third brightness block 13, and the brightness of the first brightness block 11, the second brightness block 12, and the third brightness block 13 are sequentially reduced.
  • step S2 a clustering algorithm is used to perform luminance clustering partition on the original image 10.
  • K-means clustering K-means clustering or the like may be used to perform brightness clustering partition on the original image 10.
  • Step S3 L virtual exposure images are generated using the original image 10, and the exposure degrees of the L virtual exposure images are different.
  • Each virtual exposure image has L virtual blocks corresponding to the L brightness blocks of the original image 10, respectively.
  • the position of each virtual block in the virtual exposure image where it is located is the same as the position of the corresponding brightness block in the original image 10.
  • three virtual exposure images are generated using the original image 10, which are the first virtual exposure image 20, the second virtual exposure image 30, and the third virtual exposure image 40, respectively.
  • the exposure degrees of the exposure image 20, the second virtual exposure image 30, and the third virtual exposure image 40 decrease in sequence.
  • the first virtual exposure image 20 has a first virtual block 21, a second virtual block 22 and a third corresponding to the first brightness block 11, the second brightness block 12 and the third brightness block 13 of the original image 10, respectively Three virtual blocks 23.
  • the second virtual exposure image 30 has fourth virtual blocks 31, fifth virtual blocks 32, and third corresponding to the first brightness block 11, the second brightness block 12, and the third brightness block 13 of the original image 10, respectively.
  • the third virtual exposure image 40 has a seventh virtual block 41, an eighth virtual block 42 and a third corresponding to the first brightness block 11, the second brightness block 12 and the third brightness block 13 of the original image 10, respectively Nine virtual blocks 43.
  • step S3 an exposure value of the original image is adjusted using a preset adjustment formula to generate L virtual exposure images.
  • L wk (x, y) is the brightness value at the (x, y) pixel in the kth virtual exposure image of the generated L virtual exposure images
  • L d ( x, y) is the normalized brightness value at the (x, y) pixel in the original image
  • P k is the brightness adjustment factor of the k-th virtual exposure image among the L virtual exposure images
  • L ad, k is L
  • L smax is a preset fixed value
  • L max, k is the maximum brightness value in the kth virtual exposure image among L virtual exposure images.
  • EV k is the exposure value of the k-th virtual exposure image among the L virtual exposure images
  • ⁇ is a preset constant
  • Step S4 select one of the L virtual blocks of each virtual exposure image as the virtual block to be merged, set i to a positive integer greater than 0 and less than or equal to L, according to the exposure degree from large to small
  • the virtual blocks to be merged of the i-th virtual exposure image in the L virtual exposure images are the i-th virtual area when the L virtual blocks of the i-th virtual exposure image are sorted according to the brightness from small to large Piece.
  • the third virtual block 23 with the smallest brightness in the first virtual exposure image 20 with the largest exposure is selected as the first virtual exposure image 20 to be fused Virtual block, select the fifth virtual block 32 with the medium brightness in the second virtual exposure image 30 with medium exposure as the virtual block to be merged in the second virtual exposure image 30, and select the third virtual exposure image 40 with the smallest exposure
  • the seventh virtual block 41 with the largest medium brightness is the block to be merged of the third virtual exposure image 40.
  • Step S5. Referring to FIG. 4, multiple virtual blocks to be fused are spliced to obtain the processed image 50, so that the position of each virtual block to be fused in the processed image 50 and the corresponding brightness block are in the original The positions in image 10 are the same.
  • the third virtual block 23, the fifth virtual block 32, and the seventh virtual block 41 are stitched to form an image 50 after processing.
  • step S5 when a plurality of virtual blocks to be merged are spliced, the splicing parts of two adjacent blocks to be merged are smoothed to avoid block jumps at the image joint.
  • a linear interpolation method is used to smooth the splicing parts of two adjacent blocks to be merged.
  • step S4 there is also a step of performing contrast enhancement processing on a plurality of virtual blocks to be merged to enhance the image contrast in the block.
  • a histogram equalization algorithm may be used to separately perform contrast enhancement processing on multiple virtual blocks to be fused.
  • the original image is divided into clusters to form a plurality of luminance partitions, and then the original image is used to generate a plurality of virtual exposure images with different exposure degrees, each virtual exposure image according to the original image
  • the brightness clustering partition is divided into multiple virtual blocks, and the virtual blocks to be merged are selected from the multiple virtual blocks of each virtual exposure image, and the contrast enhancement process is performed for each module to be merged separately. Fusion of virtual blocks for splicing to obtain processed images can make the image have better image details and improve the quality of the image. Compared with the existing technology, the output image fusion does not need to calculate the weight of multiple virtual exposure images. It can effectively reduce the calculation amount of image processing, reduce the overall calculation time, and increase the speed of image processing.
  • the original image is divided into brightness clusters, and the original image is used to generate a plurality of virtual exposure images with different degrees of exposure, and each virtual exposure image is divided according to the brightness cluster partition of the original image into Multiple virtual blocks, select the virtual blocks to be fused among the multiple virtual blocks of each virtual exposure image, and stitch the multiple virtual blocks to be fused to obtain the processed image, which can make the image have a better image Details, improve the quality of the image, and can increase the speed of image processing.

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Abstract

本发明提供一种图像处理方法。本发明的图像处理方法中对原始图像进行亮度聚类分区，利用原始图像生成多个曝光度不同的虚拟曝光图像，每一虚拟曝光图像依照原始图像的亮度聚类分区划分为多个虚拟区块，在每一虚拟曝光图像的多个虚拟区块中选取待融合虚拟区块后将多个待融合虚拟区块进行拼接获得处理后的图像，能够使图像具有较好的图像细节，提升图像的质量，并且能够增加图像处理的速度。

Description

图像处理方法 技术领域
本发明涉及显示技术领域，尤其涉及一种图像处理方法。
背景技术
与人眼能够看到的真实场景中的亮度范围相比，相机等数字成像设备中可用的传感器捕获到亮度范围通常要小得多。传统数字成像设备以单一曝光度对场景进行拍摄一幅图像，因而该图像只包含有限的亮度对比范围，具体为在较长的曝光时间中，曝光度较高，此时虽然被拍摄场景中的低亮度区域能够取得较为清晰的图像细节，但高亮度区域会呈现过度曝光的现象，反之，在较短的曝光时间中，曝光度较低，此时虽然被拍摄场景中的高亮度区域能够取得较为清晰的图像细节，但低亮度区域会呈现过度曝光的现象，取决于采用的曝光度是高或低，场景中过亮或较暗的区域中的很多细节将被丢失，使得数字成像设备拍摄到的图像与真实景象之间存在较大的差异，成像的品质较差。
为解决上述问题，高动态范围(High Dynamic Range，HDR)成像技术逐渐成为了数字成像设备中越来越流行的一种成像技术。通过HDR成像获得的图像也被成为HDR图像，此种HDR图像可以提供在场景中较暗区域到完全被照亮的区域之间的高亮度范围。
现有的一种图像处理方法获取多张不同曝光程度的图像，通过对多张不同曝光程度的图像进行权重融合处理，最终得到具有高亮度范围的HDR图像，此种方法由于采用多张曝光图像进行融合，图像位移问题不易处理。现有的另一种图像处理方法通过利用单一的输入图像生成多张虚拟曝光图像，之后对多张虚拟曝光图像进行权重融合处理，最终得到具有高亮度范围的HDR图像，此种方法由于采用多张虚拟曝光图像进行权重处理，计算处理的运算量较大，且无法针对个别亮度区块进行对比度增强处理。
发明内容
本发明的目的在于提供一种图像处理方法，能够使图像具有较好的图像细节，提升图像的质量，并且能够增加图像处理的速度。
为实现上述目的，本发明提供一种图像处理方法，包括如下步骤：
步骤S1、提供原始图像；
步骤S2、对所述原始图像进行亮度聚类分区，将所述原始图像划分为L个亮度区块，L个亮度区块的亮度不同，其中，L为正整数；
步骤S3、利用所述原始图像生成L个虚拟曝光图像，该L个虚拟曝光图像的曝光度不同；每一虚拟曝光图像具有分别与原始图像的L个亮度区块对应的L个虚拟区块，每一虚拟区块在其所在的虚拟曝光图像中的位置与对应的亮度区块在原始图像中的位置相同；
步骤S4、从每一虚拟曝光图像的L个虚拟区块中选取一个作为待融合虚拟区块，设i为大于0小于等于L的正整数，在依照曝光度由大到小排序时，该L个虚拟曝光图像中第i个虚拟曝光图像的待融合虚拟区块为该第i个虚拟曝光图像的L个虚拟区块按照亮度由小到大排序时的第i个虚拟区块；
步骤S5、将多个待融合虚拟区块进行拼接获得处理后的图像，使得每一待融合虚拟区块在处理后的图像中的位置与对应的亮度区块在原始图像中的位置相同。
L＝3。
所述步骤S2中，使用聚类算法对所述原始图像进行亮度聚类分区。
所述步骤S2中，采用K均值聚类的方式对所述原始图像进行亮度聚类分区。
所述步骤S3中，利用预设的调整公式对所述原始图像进行曝光值调整生成L个虚拟曝光图像。
所述调整公式为：
Figure PCTCN2019070311-appb-000001
其中，k为大于0小于等于L的正整数，L wk(x，y)为生成的L个虚拟曝光图像中第k个虚拟曝光图像中(x，y)像素处的亮度值；L d(x，y)为所述原始图像中(x，y)像素处的归一化亮度值；P k为L个虚拟曝光图像中第k个虚拟曝光图像的亮度调节因子，L ad，k为L个虚拟曝光图像中第k个虚拟曝光图像的平均亮度值，L smax为预设的固定值，L max，k为L个虚拟曝光图像中第k个虚拟曝光图像中最大的亮度值；
L ad，k的计算公式为：
L ad，k＝1+exp(μEV k)；
其中，EV k为L个虚拟曝光图像中第k个虚拟曝光图像的曝光值，μ为预设的常数。
所述步骤S5中在将多个待融合虚拟区块进行拼接时，对相邻两个待融合区块的拼接部分进行平滑处理。
采用线性内插的方式对相邻两个待融合区块的拼接部分进行平滑处理。
所述步骤S4与步骤S5之间还具有分别对多个待融合虚拟区块进行增强对比度处理的步骤。
采用直方图均衡化算法分别对多个待融合虚拟区块进行增强对比度处理。
本发明的有益效果：本发明的图像处理方法中对原始图像进行亮度聚类分区，利用原始图像生成多个曝光度不同的虚拟曝光图像，每一虚拟曝光图像依照原始图像的亮度聚类分区划分为多个虚拟区块，在每一虚拟曝光图像的多个虚拟区块中选取待融合虚拟区块后将多个待融合虚拟区块进行拼接获得处理后的图像，能够使图像具有较好的图像细节，提升图像的质量，并且能够增加图像处理的速度。
附图说明
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。
附图中，
图1为本发明的图像处理方法的流程图；
图2为本发明的图像处理方法的一实施例的步骤S2的示意图；
图3为本发明的图像处理方法的一实施例的步骤S3的示意图；
图4为本发明的图像处理方法的一实施例的步骤S4及步骤S5的示意图。
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1，本发明提供一种图像处理方法，包括如下步骤：
步骤S1、请参阅图2，提供原始图像10。
步骤S2、请参阅图2，对所述原始图像10进行亮度聚类分区，将所述原始图像10划分为L个亮度区块，L个亮度区块的亮度不同，其中，L为正整数。
具体地，在图2所示的实施例中，L＝3，也即在所述步骤S2中，对原始图像10进行亮度聚类分区之后，将所述原始图像10划分为3个亮度区块，分别为第一亮度区块11、第二亮度区块12及第三亮度区块13，第一亮度区块11、第二亮度区块12及第三亮度区块13的亮度依次减小。
具体地，所述步骤S2中，使用聚类算法对所述原始图像10进行亮度聚类分区。
进一步地，所述步骤S2中，可以采用K均值聚类(K-means Clustering)等方式对所述原始图像10进行亮度聚类分区。
步骤S3、请参阅图3，利用所述原始图像10生成L个虚拟曝光图像，该L个虚拟曝光图像的曝光度不同。每一虚拟曝光图像具有分别与原始图像10的L个亮度区块对应的L个虚拟区块。每一虚拟区块在其所在的虚拟曝光图像中的位置与对应的亮度区块在原始图像10中的位置相同。
具体地，在图3所示的实施例中，利用原始图像10生成3个虚拟曝光图像，分别为第一虚拟曝光图像20、第二虚拟曝光图像30及第三虚拟曝光图像40，第一虚拟曝光图像20、第二虚拟曝光图像30及第三虚拟曝光图像40的曝光度依次减小。第一虚拟曝光图像20具有分别与原始图像10的第一亮度区块11、第二亮度区块12及第三亮度区块13对应的第一虚拟区块21、第二虚拟区块22及第三虚拟区块23。第二虚拟曝光图像30具有分别与原始图像10的第一亮度区块11、第二亮度区块12及第三亮度区块13对应的第四虚拟区块31、第五虚拟区块32及第六虚拟区块33。第三虚拟曝光图像40具有分别与原始图像10的第一亮度区块11、第二亮度区块12及第三亮度区块13对应的第七虚拟区块41、第八虚拟区块42及第九虚拟区块43。
具体地，所述步骤S3中，利用预设的调整公式对所述原始图像进行曝光值调整生成L个虚拟曝光图像。
进一步地，所述调整公式为：
Figure PCTCN2019070311-appb-000002
其中，k为大于0小于等于L的正整数，L wk(x，y)为生成的L个虚拟 曝光图像中第k个虚拟曝光图像中(x，y)像素处的亮度值；L d(x，y)为所述原始图像中(x，y)像素处的归一化亮度值；P k为L个虚拟曝光图像中第k个虚拟曝光图像的亮度调节因子，L ad，k为L个虚拟曝光图像中第k个虚拟曝光图像的平均亮度值，L smax为预设的固定值，L max，k为L个虚拟曝光图像中第k个虚拟曝光图像中最大的亮度值。
L ad，k的计算公式为：
L ad，k＝1+exp(μEV k)。
其中，EV k为L个虚拟曝光图像中第k个虚拟曝光图像的曝光值，μ为预设的常数。
通过改变调整公式中EV k及P k的取值结合原始图像即可生成多个曝光度不同的虚拟曝光图像。
步骤S4、请参阅图4，从每一虚拟曝光图像的L个虚拟区块中选取一个作为待融合虚拟区块，设i为大于0小于等于L的正整数，在依照曝光度由大到小排序时，该L个虚拟曝光图像中第i个虚拟曝光图像的待融合虚拟区块为该第i个虚拟曝光图像的L个虚拟区块按照亮度由小到大排序时的第i个虚拟区块。
具体地，在图4所示的实施例中，所述步骤S4中，选取曝光度最大的第一虚拟曝光图像20中亮度最小的第三虚拟区块23为第一虚拟曝光图像20的待融合虚拟区块，选取曝光度中等的第二虚拟曝光图像30中亮度中等的第五虚拟区块32为第二虚拟曝光图像30的待融合虚拟区块，选取曝光度最小的第三虚拟曝光图像40中亮度最大的第七虚拟区块41为第三虚拟曝光图像40的待融合区块。
步骤S5、请参阅图4，将多个待融合虚拟区块进行拼接获得处理后的图像50，使得每一待融合虚拟区块在处理后的图像50中的位置与对应的亮度区块在原始图像10中的位置相同。
具体地，在图4所示的实施例中，所述步骤S5中将第三虚拟区块23、第五虚拟区块32及第七虚拟区块41进行拼接形成处理后的图像50。
具体地地，所述步骤S5中在将多个待融合虚拟区块进行拼接时，对相邻两个待融合区块的拼接部分进行平滑处理，避免图像接合处产生区块跳 变。
优选地，采用线性内插的方式对相邻两个待融合区块的拼接部分进行平滑处理。
具体地，所述步骤S4与步骤S5之间还具有分别对多个待融合虚拟区块进行增强对比度处理的步骤，以增强区块内的图像对比度。
优选地，可采用直方图均衡化算法分别对多个待融合虚拟区块进行增强对比度处理。
需要说明的是，本发明的图像处理方法中对原始图像进行亮度聚类分区从而形成多个亮度分区，而后利用原始图像生成多个曝光度不同的虚拟曝光图像，每一虚拟曝光图像依照原始图像的亮度聚类分区划分为多个虚拟区块，在每一虚拟曝光图像的多个虚拟区块中选取待融合虚拟区块后对每一待融合模块单独进行增强对比度处理，最后将多个待融合虚拟区块进行拼接获得处理后的图像，能够使图像具有较好的图像细节，提升图像的质量，并且相比于现有技术，输出图像融合时无需对多张虚拟曝光图像进行权重计算，能够有效减少图像处理的运算量，减少整体的运算时间，增加图像处理的速度。
综上所述，本发明的图像处理方法中对原始图像进行亮度聚类分区，利用原始图像生成多个曝光度不同的虚拟曝光图像，每一虚拟曝光图像依照原始图像的亮度聚类分区划分为多个虚拟区块，在每一虚拟曝光图像的多个虚拟区块中选取待融合虚拟区块后将多个待融合虚拟区块进行拼接获得处理后的图像，能够使图像具有较好的图像细节，提升图像的质量，并且能够增加图像处理的速度。
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 (10)

  1. 一种图像处理方法，包括如下步骤：
    步骤S1、提供原始图像；
    步骤S2、对所述原始图像进行亮度聚类分区，将所述原始图像划分为L个亮度区块，L个亮度区块的亮度不同，其中，L为正整数；
    步骤S3、利用所述原始图像生成L个虚拟曝光图像，该L个虚拟曝光图像的曝光度不同；每一虚拟曝光图像具有分别与原始图像的L个亮度区块对应的L个虚拟区块；每一虚拟区块在其所在的虚拟曝光图像中的位置与对应的亮度区块在原始图像中的位置相同；
    步骤S4、从每一虚拟曝光图像的L个虚拟区块中选取一个作为待融合虚拟区块，设i为大于0小于等于L的正整数，在依照曝光度由大到小排序时，该L个虚拟曝光图像中第i个虚拟曝光图像的待融合虚拟区块为该第i个虚拟曝光图像的L个虚拟区块按照亮度由小到大排序时的第i个虚拟区块；
    步骤S5、将多个待融合虚拟区块进行拼接获得处理后的图像，使得每一待融合虚拟区块在处理后的图像中的位置与对应的亮度区块在原始图像中的位置相同。
  2. 如权利要求1所述的图像处理方法，其中，L＝3。
  3. 如权利要求1所述的图像处理方法，其中，所述步骤S2中，使用聚类算法对所述原始图像进行亮度聚类分区。
  4. 如权利要求3所述的图像处理方法，其中，所述步骤S2中，采用K均值聚类的方式对所述原始图像进行亮度聚类分区。
  5. 如权利要求1所述的图像处理方法，其中，所述步骤S3中，利用预设的调整公式对所述原始图像进行曝光值调整生成L个虚拟曝光图像。
  6. 如权利要求5所述的图像处理方法，其中，所述调整公式为：
    Figure PCTCN2019070311-appb-100001
    其中，k为大于0小于等于L的正整数，L wk(x，y)为生成的L个虚拟曝光图像中第k个虚拟曝光图像中(x，y)像素处的亮度值；L d(x，y)为所述原始图像中(x，y)像素处的归一化亮度值；P k为L个虚拟曝光图像中第k个 虚拟曝光图像的亮度调节因子，L ad，k为L个虚拟曝光图像中第k个虚拟曝光图像的平均亮度值，L smax为预设的固定值，L max，k为L个虚拟曝光图像中第k个虚拟曝光图像中最大的亮度值；
    L ad，k的计算公式为：
    L ad，k＝1+exp(μEV k)；
    其中，EV k为L个虚拟曝光图像中第k个虚拟曝光图像的曝光值，μ为预设的常数。
  7. 如权利要求1所述的图像处理方法，其中，所述步骤S5中在将多个待融合虚拟区块进行拼接时，对相邻两个待融合区块的拼接部分进行平滑处理。
  8. 如权利要求7所述的图像处理方法，其中，采用线性内插的方式对相邻两个待融合区块的拼接部分进行平滑处理。
  9. 如权利要求1所述的图像处理方法，其中，所述步骤S4与步骤S5之间还具有分别对多个待融合虚拟区块进行增强对比度处理的步骤。
  10. 如权利要求9所述的图像处理方法，其中，采用直方图均衡化算法分别对多个待融合虚拟区块进行增强对比度处理。
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