CN103106644B - Overcome the self-adaptation picture quality enhancement method of coloured image inhomogeneous illumination - Google Patents
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Abstract
本发明公开了一种克服彩色图像非均匀光照的自适应画质增强方法。该方法先计算亮度图像;利用亮度图像中像素邻域内的平均强度值计算局部亮度指标;建立局部亮度指标与对比度增强系数之间的映射关系;用对比度增强系数构造单通道图像像素间对比度差异度量,进而加权整合为全局自适应对比度能量项;将该全局自适应能量与熵离差项组成图像增强的代价最小化模型;最后利用梯度下降法求解该模型的最小值作为增强后的单通道图像;所有通道都处理完毕后,将全部增强后的单通道图像合并成输出的彩色图像。本发明在有效提升图像对比度和去除色偏的同时,能消除过亮和过暗的光照不均匀效果,保持明暗区域中物体的细节完整性。
The invention discloses an adaptive image quality enhancement method for overcoming non-uniform illumination of color images. The method first calculates the brightness image; uses the average intensity value in the pixel neighborhood of the brightness image to calculate the local brightness index; establishes the mapping relationship between the local brightness index and the contrast enhancement coefficient; uses the contrast enhancement coefficient to construct a single-channel image pixel contrast difference measure , and then weighted and integrated into a global adaptive contrast energy item; the global adaptive energy and entropy deviation item constitute the cost minimization model of image enhancement; finally, the gradient descent method is used to solve the minimum value of the model as the enhanced single-channel image ; After all channels are processed, merge all enhanced single-channel images into an output color image. While effectively improving image contrast and removing color cast, the invention can eliminate the uneven illumination effect of over-brightness and over-darkness, and maintain the integrity of details of objects in bright and dark areas.
Description
技术领域technical field
本发明属于图像增强技术领域,特别是一种克服彩色图像非均匀光照的自适应画质增强方法。The invention belongs to the technical field of image enhancement, in particular to an adaptive image quality enhancement method for overcoming non-uniform illumination of color images.
背景技术Background technique
人类视觉系统具有局部自适应的特性,在观察自然场景时,人类能够通过收缩放大瞳孔来调节进光量,并通过视网膜和大脑皮层进行光照强度的动态范围压缩,最终分辨出不同光照下的物体。然而成像设备无法像人类视觉系统那样适应各种复杂条件下的光照环境,呈现出自然场景中物体的真实色彩,这不利于我们在日常生活和科学研究中对图像进行分析、识别。因此,模拟人类视觉系统克服彩色图像非均匀光照,自适应地增强图像画质具有重要的实际应用价值。The human visual system has the characteristic of local adaptation. When observing natural scenes, humans can adjust the amount of incoming light by constricting and dilating the pupil, and compress the dynamic range of light intensity through the retina and cerebral cortex, and finally distinguish objects under different lighting conditions. However, imaging equipment cannot adapt to the lighting environment under various complex conditions like the human visual system, and present the true colors of objects in natural scenes, which is not conducive to our image analysis and recognition in daily life and scientific research. Therefore, simulating the human visual system to overcome the non-uniform illumination of color images and adaptively enhance image quality has important practical application value.
最早在模拟人类视觉系统方面做出贡献的是Land和McCann,他们通过一系列色彩感知实验提出了著名的Retinex理论。基于Retinex理论,陶理等人提出一种综合的邻域依赖非线性增强算法(LiTao,K.VijayanAsari,Anintegratedneighborhooddependentapproachfornonlinearenhancementofcolorimages,ProceedingsoftheIEEEComputerSocietyInternationalConferenceonInformationTechnology:CodingandComputing(ITCC'04),vol.2,pp.138-139,2004),先后对亮度和对比度进行增强,以达到压缩动态范围、改善阴暗区域视觉效果的目的,然而算法中的全局对比度增强会使明亮区域更亮而阴暗区域更暗,造成图像细节丢失。此外,与单尺度Retinex算法和多尺度Retinex算法一样,这些基于Retinex理论的图像增强方法都依赖于高斯函数与原图之间的卷积运算,这会增加算法的复杂度,并且会在明暗交界亮度变化较大的区域引起光晕现象。中国专利[200810116385.2]发明了一种基于Retinex理论的快速彩色图像增强方法,通过将卷积运算简化为均值运算,降低了算法复杂度,然而均值运算依然会引起光晕现象,且该方法中对彩色图像三个通道中的最大值构成的图像进行处理,会丢失各分量通道中低强度像素所在的阴暗区域中包含的图像信息。中国专利[201110316982.1]发明了一种不均匀光照下的棉花伪异性纤维彩色图像增强方法及系统,将彩色图像从RGB空间转换到HSI空间后,通过最小化关于I分量的Retinex变分模型和Gamma校正对I分量进行增强,再将增强后的图像转换回RGB空间,虽然该方法用变分模型避免了卷积运算,然而该方法中没有模拟人类视觉系统针对非均匀光照做自适应处理。Land and McCann were the first to make contributions to simulating the human visual system. They proposed the famous Retinex theory through a series of color perception experiments. Based on the Retinex theory, Tao Li et al. proposed a comprehensive neighborhood dependent nonlinear enhancement algorithm (LiTao, K.Vijayan Asari, An integrated neighborhood dependent approach for nonlinear enhancement of color images, Proceeding of the IEEE Computer Society International Conference on Information Technology: Coding and Computing (ITCC'04), vol. 2, pp. 138-0439), , the brightness and contrast are enhanced successively to achieve the purpose of compressing the dynamic range and improving the visual effect of dark areas. However, the global contrast enhancement in the algorithm will make bright areas brighter and dark areas darker, resulting in loss of image details. In addition, like the single-scale Retinex algorithm and the multi-scale Retinex algorithm, these image enhancement methods based on the Retinex theory all rely on the convolution operation between the Gaussian function and the original image, which will increase the complexity of the algorithm, and will cause problems at the junction of light and shade. Areas with large changes in brightness cause the halo phenomenon. Chinese patent [200810116385.2] invented a fast color image enhancement method based on the Retinex theory. By simplifying the convolution operation into the mean value operation, the complexity of the algorithm is reduced. However, the mean value operation still causes halo phenomenon, and the method Processing the image formed by the maximum value of the three channels of the color image will lose the image information contained in the dark area where the low-intensity pixels in each component channel are located. Chinese patent [201110316982.1] invented a method and system for color image enhancement of cotton pseudo-heterosexual fibers under uneven illumination. After converting the color image from RGB space to HSI space, by minimizing the Retinex variational model and Gamma of the I component Correction enhances the I component, and then converts the enhanced image back to RGB space. Although this method uses a variational model to avoid convolution operations, this method does not simulate the adaptive processing of the human visual system for non-uniform illumination.
发明内容Contents of the invention
本发明的目的在于提供一种克服彩色图像非均匀光照的自适应画质增强方法,构造了彩色图像增强的代价最小化模型,并通过计算局部亮度指标来判断图像像素所在区域的明暗程度,使模型能根据图像的局部亮度做自适应处理,从而使彩色图像在增强后明暗区域中的细节都能够得到很好的保持。The purpose of the present invention is to provide an adaptive image quality enhancement method that overcomes the non-uniform illumination of color images, constructs a cost minimization model for color image enhancement, and judges the brightness and darkness of the area where the image pixels are located by calculating the local brightness index, so that The model can do adaptive processing according to the local brightness of the image, so that the details of the color image can be well preserved in the bright and dark areas after enhancement.
实现本发明目的的技术解决方案为:一种克服彩色图像非均匀光照的自适应画质增强方法,计算亮度图像,利用亮度图像中像素邻域内的平均强度值计算局部亮度指标a(x);建立局部亮度指标a(x)与对比度增强系数λ(x)之间的映射关系,即λ(x)=1/(1+exp(5-10a(x)));用对比度增强系数构造单通道图像像素间对比度差异度量;用像素间对比度差异度量加权整合为单通道图像全局自适应对比度能量项;将该全局自适应能量与熵离差项组成图像增强的代价最小化模型;最后利用梯度下降法求解该模型的最小值作为增强后的单通道图像,所有通道都处理完毕后,将全部增强后的单通道图像合并成输出的彩色图像。The technical solution for realizing the purpose of the present invention is: an adaptive image quality enhancement method for overcoming non-uniform illumination of color images, calculating the brightness image, and calculating the local brightness index a(x) by using the average intensity value in the neighborhood of pixels in the brightness image; Establish the mapping relationship between the local brightness index a(x) and the contrast enhancement coefficient λ(x), that is, λ(x)=1/(1+exp(5-10a(x))); use the contrast enhancement coefficient to construct a single Contrast difference measurement between channel image pixels; weighted integration of the contrast difference measurement between pixels into a single-channel image global adaptive contrast energy item; the cost minimization model of image enhancement is composed of the global adaptive energy and entropy deviation item; finally using the gradient The descent method solves the minimum value of the model as an enhanced single-channel image. After all channels are processed, all enhanced single-channel images are combined into an output color image.
本发明与现有技术相比,其显著优点为:(1)将人类视觉系统的特性与变分技术结合在一起,能够根据图像局部区域的明暗程度自适应地增强图像。(2)能在有效提升图像画质对比度的同时,去除彩色图像的色彩偏差。(3)具有更好的图像动态范围调节能力。Compared with the prior art, the present invention has the following remarkable advantages: (1) Combining the characteristics of the human visual system with the variational technology, the image can be adaptively enhanced according to the brightness and darkness of the local area of the image. (2) While effectively improving the contrast of the image quality, the color deviation of the color image can be removed. (3) It has better image dynamic range adjustment capability.
下面结合附图对本发明作进一步详细描述。The present invention will be described in further detail below in conjunction with the accompanying drawings.
附图说明Description of drawings
图1是本发明克服彩色图像非均匀光照的自适应画质增强方法的流程图。Fig. 1 is a flowchart of an adaptive image quality enhancement method for overcoming non-uniform illumination of a color image according to the present invention.
图2是本发明中局部亮度指标a(x)与对比度增强系数λ(x)之间的映射关系示意图。Fig. 2 is a schematic diagram of the mapping relationship between the local brightness index a(x) and the contrast enhancement coefficient λ(x) in the present invention.
图3(a)是本发明实施例中的测试图像“clothes3”图(大小为637×468),图3(b)是图3(a)的三个通道的直方图。Figure 3(a) is the test image "clothes3" (size 637×468) in the embodiment of the present invention, and Figure 3(b) is the histogram of the three channels in Figure 3(a).
图4(a)是本发明实施例中的测试图像“clothes4”图(大小为637×468),图4(b)是图4(a)的三个通道的直方图。Figure 4(a) is a test image "clothes4" (size 637×468) in the embodiment of the present invention, and Figure 4(b) is a histogram of the three channels in Figure 4(a).
图5(a)是本发明实施例中对“clothes3”图增强后的结果图像,图5(b)是图5(a)的三个通道的直方图。Fig. 5(a) is the result image after enhancing the "clothes3" image in the embodiment of the present invention, and Fig. 5(b) is the histogram of the three channels in Fig. 5(a).
图6(a)是本发明实施例中对“clothes4”图增强后的结果图像,图6(b)是图6(a)的三个通道的直方图。Fig. 6(a) is the result image after enhancing the "clothes4" image in the embodiment of the present invention, and Fig. 6(b) is the histogram of the three channels in Fig. 6(a).
具体实施方式detailed description
本发明克服彩色图像非均匀光照的自适应画质增强方法以变分技术和人类视觉系统特性作为模型的建模手段和理论基础。变分技术是图像处理中常用的方法,其能量泛函的选择取决于要处理的问题,使泛函达到最小值的解就是问题最后的结果图像,变分技术虽然更容易理解和解决图像处理问题,但如何应用于克服彩色图像非均匀光照的自适应画质增强是一个技术难题。另外,由于人类视觉系统对物体细节的感知依赖于物体所处的空间位置,具有局部性,因此对比度的增强也应该具有局部性,本发明方法构造符合人类视觉系统特性的彩色图像增强的代价最小化模型。对彩色图像中任意一副单通道图像I:Ω→[0,1],令x=(x1,x2)和y=(y1,y2)表示I中任意两个像素的坐标,I0表示原始图像。根据人类视觉系统的对比度增强局部性和视觉适应性,色彩校正能量泛函应满足如下形式:The self-adaptive image quality enhancement method for overcoming non-uniform illumination of color images in the present invention uses variational technology and human visual system characteristics as modeling means and theoretical basis of the model. Variational technology is a commonly used method in image processing. The choice of its energy functional depends on the problem to be processed. The solution that makes the functional reach the minimum value is the final result image of the problem. Although variational technology is easier to understand and solve image processing However, how to apply it to the adaptive image quality enhancement that overcomes the non-uniform illumination of color images is a technical problem. In addition, since the human visual system's perception of object details depends on the spatial position of the object and is localized, the enhancement of contrast should also be localized. The method of the present invention constructs a color image enhancement that conforms to the characteristics of the human visual system with the least cost model. For any pair of single-channel images in color images I:Ω→[0,1], Let x=(x 1 , x 2 ) and y=(y 1 , y 2 ) represent the coordinates of any two pixels in I, and I 0 represents the original image. According to the contrast enhancement locality and visual adaptability of the human visual system, the color correction energy functional should satisfy the following form:
Ew(I)=Cw(I)+D(I),(1)E w (I) = C w (I) + D (I), (1)
其中对比度能量项Cw(I)的形式为权重函数起着局部化对比度计算的作用,c(I(x),I(y))是基本对比度差异度量。c(I(x),I(y))中含有基本对比度变量
通过梯度下降法寻求最小值,则我们可以得到图像I的最优估计I*,最后将增强后的三个通道图像合并成输出的彩色图像。By seeking the minimum value through the gradient descent method, we can obtain the optimal estimate I * of the image I, and finally merge the enhanced three-channel images into an output color image.
实现上述内容的具体步骤为:The specific steps to achieve the above content are:
步骤1:计算亮度图像。将一幅待增强彩色图像各通道的像素强度都归一化到[0,1]区间内,根据NTSC(国家电视制式委员会)标准计算彩色图像的亮度图像:L=0.2989×R+0.587×G+0.144×B,其中R、G、B分别为彩色图像的红绿蓝三个通道的图像;Step 1: Compute the brightness image. A color image to be enhanced The pixel intensity of each channel is normalized to the [0,1] interval, and the brightness image of the color image is calculated according to the NTSC (National Television System Committee) standard: L=0.2989×R+0.587×G+0.144×B, where R , G, B are color images respectively The image of the three channels of red, green and blue;
步骤2:计算局部亮度指示指标。根据亮度图像L,计算以每个像素点x=(x1,x2)为中心、大小为5×5像素的邻域中的像素强度平均值a(x),其中(x1,x2)表示L中任意一个像素x的坐标,对于图像边缘处的像素点用对称的方式填充邻域中的像素,a(x)即为局部亮度指标,取值范围为[0,1],当0≤a(x)≤1/3时,认为像素点x位于低亮度的区域内,当1/3<a(x)<2/3时,认为像素点x位于中等亮度的区域内,当2/3≤a(x)≤1时,认为像素点x位于高亮度的区域内;Step 2: Calculate the local brightness indicator. According to the brightness image L, calculate the average pixel intensity a(x) in the neighborhood of each pixel point x=(x 1 ,x 2 ) with a size of 5×5 pixels, Where (x 1 , x 2 ) represents the coordinates of any pixel x in L, and the pixels at the edge of the image are filled with pixels in the neighborhood in a symmetrical manner, a(x) is the local brightness index, and the value range is [0,1], when 0≤a(x)≤1/3, the pixel point x is considered to be in the area of low brightness; when 1/3<a(x)<2/3, the pixel point x is considered to be in the In the area of medium brightness, when 2/3≤a(x)≤1, the pixel point x is considered to be in the area of high brightness;
步骤3:计算对比度增强系数。利用局部亮度指标a(x)计算对比度增强系数λ(x)之间的映射关系λ(x)=1/(1+exp(5-10a(x)));Step 3: Calculate the contrast enhancement coefficient. Using the local brightness index a(x) to calculate the mapping relationship between the contrast enhancement coefficient λ(x) λ(x)=1/(1+exp(5-10a(x)));
步骤4:构造单通道图像像素间对比度差异度量。对的某个通道的图像I,用对比度增强系数λ(x)和基本对比度变量tε构造像素间对比度差异度量,其计算公式为:
其中:
式中
步骤5:构造单通道图像全局加权自适应对比度能量项。通过对像素间对比度差异度量进行加权整合,得到自适应对比度能量项,其计算公式为:Step 5: Construct a globally weighted adaptive contrast energy term for a single-channel image. The adaptive contrast energy term is obtained by weighted integration of the inter-pixel contrast difference measure, and its calculation formula is:
其中:
步骤6:形成单通道图像增强最小化代价模型(函数)。通过耦合图像全局加权自适应对比度能量和熵离差度量建立单通道图像增强的最小化代价函数,Step 6: Form a single-channel image enhancement minimization cost model (function). Adaptive contrast energy by coupling image global weighting and the entropy dispersion measure Establish a minimized cost function for single-channel image enhancement,
其中熵离差度量定义为:where the entropy dispersion measure is defined as:
式中I0是原始单通道图像,α,β>0是用于权衡关于I与1/2的离差函数项和关于I与I0的离差函数项的参数;In the formula, I 0 is the original single-channel image, and α, β > 0 are parameters used to weigh the dispersion function item about I and 1/2 and the dispersion function item about I and I 0 ;
步骤7:逐通道最小化求解。利用梯度下降法求解该最小化代价函数,将求得的最小值I*作为增强后的单通道图像。判断当前处理的通道是否为图像的最后一个通道,如果不是最后一个通道,则重复步骤4至步骤7处理下一个通道的图像,如果是最后一个通道,则进行步骤8;Step 7: Minimize solution channel by channel. The gradient descent method is used to solve the minimized cost function, and the obtained minimum value I * is used as the enhanced single-channel image. Determine whether the currently processed channel is the last channel of the image, if not the last channel, then repeat steps 4 to 7 to process the image of the next channel, if it is the last channel, go to step 8;
步骤8:彩色图像合成。所有通道都处理完后,将全部增强后的单通道图像合并成输出的彩色图像。Step 8: Color image synthesis. After all channels are processed, all enhanced single-channel images are merged into an output color image.
下面结合附图和实施例,对本发明的实施过程进行如下详细说明。在本次实施例中采用SimonFraserUniversity的图像数据库(http://www.cs.sfu.ca/~colour/data/colour_constancy_test_images/mondrian/index.html)中的2幅大小为637×468的图像“clothes3”图和“clothes4”图进行实验,该数据库中的数据是在不同光照条件下对不同物体拍摄而成的彩色图像,具有色偏、低对比度、低色调、细节不突出等特点。The implementation process of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments. In this embodiment, two images with a size of 637×468 “clothes3” in the image database (http://www.cs.sfu.ca/~colour/data/colour_constancy_test_images/mondrian/index.html) of Simon Fraser University are used. " and "clothes4" images are used for experiments. The data in this database are color images taken of different objects under different lighting conditions, which have the characteristics of color cast, low contrast, low tone, and inconspicuous details.
如图1所示,首先输入待增强的“clothes3”图(或“clothes4”图),如图3(a)(或图4(a))所示,将其记为然后进行以下步骤:As shown in Figure 1, first input the "clothes3" image (or "clothes4" image) to be enhanced, as shown in Figure 3(a) (or Figure 4(a)), and record it as Then proceed to the following steps:
步骤1:计算亮度图像。将彩色图像各通道的像素强度都归一化到[0,1]区间内,根据NTSC(国家电视制式委员会)标准计算彩色图像的亮度图像:Step 1: Compute the brightness image. color image The pixel intensity of each channel is normalized to the [0,1] interval, and the brightness image of the color image is calculated according to the NTSC (National Television System Committee) standard:
L=0.2989×R+0.587×G+0.144×B,L=0.2989×R+0.587×G+0.144×B,
其中R、G、B分别为彩色图像的红绿蓝三个通道的图像;Where R, G, and B are color images respectively The image of the three channels of red, green and blue;
步骤2:计算局部亮度指示指标。根据亮度图像L,计算以每个像素点x=(x1,x2)为中心、大小为5×5像素的邻域中的像素强度平均值对于图像边缘处的像素点用对称的方式填充邻域中的像素。a(x)即为局部亮度指标,取值范围为[0,1],当0≤a(x)≤1/3时,认为像素点x位于低亮度的区域内,当1/3<a(x)<2/3时,认为像素点x位于中等亮度的区域内,当2/3≤a(x)≤1时,认为像素点x位于高亮度的区域内;Step 2: Calculate the local brightness indicator. According to the brightness image L, calculate the average pixel intensity in the neighborhood of each pixel x=(x 1 ,x 2 ) with a size of 5×5 pixels For the pixels at the edge of the image, the pixels in the neighborhood are filled in a symmetrical manner. a(x) is the local brightness index, and the value range is [0,1]. When 0≤a(x)≤1/3, the pixel point x is considered to be in a low-brightness area, and when 1/3<a (x)<2/3, the pixel point x is considered to be in the area of medium brightness, and when 2/3≤a(x)≤1, the pixel point x is considered to be in the area of high brightness;
步骤3:计算对比度增强系数。建立局部亮度指标a(x)与对比度增强系数λ(x)之间的映射关系λ(x)=1/(1+exp(5-10a(x))),使得当像素点x位于低亮度的区域时,λ(x)的值较小,当像素点x位于高亮度的区域时,λ(x)的值较大。Step 3: Calculate the contrast enhancement coefficient. Establish a mapping relationship between the local brightness index a(x) and the contrast enhancement coefficient λ(x) λ(x)=1/(1+exp(5-10a(x))), so that when the pixel point x is located at low brightness When the pixel point x is located in a high brightness area, the value of λ(x) is small, and the value of λ(x) is large.
如图2所示;as shown in picture 2;
下面对中三个通道的图像分别进行步骤4至步骤7中的处理:next to The images of the three channels in are processed in step 4 to step 7 respectively:
步骤4:构造单通道图像像素间对比度差异度量。对的某个通道的图像I,用对比度增强系数λ(x)和基本对比度变量tε构造自适应局部亮度的对比度差异度量:Step 4: Construct a single-channel image pixel-to-pixel contrast difference metric. right For an image I of a certain channel of , use the contrast enhancement coefficient λ(x) and the basic contrast variable t ε to construct a contrast difference metric for adaptive local brightness:
其中:in:
步骤5:构造单通道图像全局加权自适应对比度能量项。通过对像素间对比度差异度量进行加权整合,得到自适应对比度能量项,其计算公式为:Step 5: Construct a global weighted adaptive contrast energy term for a single-channel image. The adaptive contrast energy term is obtained by weighted integration of the contrast difference measures between pixels, and its calculation formula is:
其中:in:
由于对比度增强系数λ(x)的调节作用,在图像中低亮度的区域与更接近,在图像中高亮度的区域与更接近;Due to the adjustment effect of the contrast enhancement coefficient λ(x), In areas of low brightness in the image and Closer, the areas of high brightness in the image are associated with Closer;
步骤6:形成单通道图像增强最小化代价函数。通过耦合图像全局加权自适应对比度能量和熵离差度量建立单通道图像增强的最小化代价函数:Step 6: Form a single-channel image enhancement to minimize the cost function. Adaptive contrast energy by coupling image global weighting and the entropy dispersion measure Create a minimized cost function for single-channel image enhancement:
其中熵离差度量定义为:where the entropy dispersion measure is defined as:
式中I0是原始单通道图像,α,β>0是用于权衡关于I与1/2的离差函数项和关于I与I0的离差函数项的参数,在本次实施例中设置为α=255/253,β=1;In the formula, I 0 is the original single-channel image, α, β>0 is used to weigh the parameters of the dispersion function item about I and 1/2 and about the dispersion function item of I and I 0 , in this embodiment Set to α=255/253, β=1;
步骤7:逐通道最小化求解。利用梯度下降法求解该最小化代价函数,将求得的最小值I*作为增强后的单通道图像。判断当前处理的通道是否为图像的最后一个通道,如果不是最后一个通道,则重复步骤4至步骤7处理下一个通道的图像,如果是最后一个通道,则进行步骤8;Step 7: Minimize solution channel by channel. The gradient descent method is used to solve the minimized cost function, and the obtained minimum value I * is used as the enhanced single-channel image. Determine whether the currently processed channel is the last channel of the image, if not the last channel, then repeat steps 4 to 7 to process the image of the next channel, if it is the last channel, then proceed to step 8;
步骤8:彩色图像合成。三个通道都处理完后,将全部增强后的单通道图像合并成输出的彩色图像,如图5(a)(或图6(a))所示。Step 8: Color image synthesis. After all three channels are processed, all enhanced single-channel images are merged into an output color image, as shown in Fig. 5(a) (or Fig. 6(a)).
下面结合图3至图6,通过实施例的效果评价来进一步说明本发明。The present invention will be further described through the effect evaluation of the embodiments below in conjunction with FIG. 3 to FIG. 6 .
如图3(a)和图4(a)所示,原始的“clothes3”图和“clothes4”图都含有一些纹理细节,其中“clothes3”图像有严重的蓝色色偏,很难看出图中衣物原先的色彩;“clothes4”图像色调偏暗,对比度低。从图3(b)和图4(b)中还可以看出,这两幅图三个通道的直方图都非常不均匀,大多数像素都集中在0到50的强度范围之间。图5(a)为本发明方法处理图3(a)后得到的增强结果,从图中可以看出,原先的严重蓝色色偏已经去除了,呈现出了衣物原先的真实色彩,衣物上的褶皱纹理等细节部分也很好地还原了出来。图6(a)为本发明方法处理图4(a)后得到的增强结果,从图中可以看出,原先灰暗的图片变得鲜艳明亮,衣物上的彩色条纹也清晰可见,褶皱纹理等细节也很好地还原出来。另外从两幅结果图像三个通道的直方图(图5(b)和图6(b))中也可以看出,像素强度的分布范围被拉伸了,直方图比原先更加均匀。As shown in Figure 3(a) and Figure 4(a), the original "clothes3" image and "clothes4" image both contain some texture details, and the "clothes3" image has a serious blue color cast, and it is difficult to see the clothes in the image Original colors; "clothes4" image has darker tones and low contrast. It can also be seen from Fig. 3(b) and Fig. 4(b) that the histograms of the three channels of these two images are very uneven, and most pixels are concentrated between the intensity range of 0 to 50. Fig. 5(a) is the enhancement result obtained after processing Fig. 3(a) by the method of the present invention. It can be seen from the figure that the original serious blue color cast has been removed, showing the original true color of the clothes, and the Details such as wrinkled textures are also well restored. Figure 6(a) is the enhancement result obtained after processing Figure 4(a) by the method of the present invention. It can be seen from the figure that the original gray and dark picture becomes bright and bright, and the colored stripes on the clothes are also clearly visible, and details such as wrinkle texture Also restored nicely. In addition, it can also be seen from the histograms of the three channels of the two result images (Figure 5(b) and Figure 6(b)), the distribution range of pixel intensity is stretched, and the histogram is more uniform than before.
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