CN104318525A - Space guiding filtering based image detail enhancement method - Google Patents
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Abstract
本发明公开了一种基于空间引导滤波的图像细节增强方法,其特征是按如下步骤进行:首先,利用图像边缘检测算子对源图像提取边缘特征响应图,并进行归一化;然后,对于不同的灰度区间分别建立二值化的空间指示图,并对每个空间指示图进行高斯卷积,得到空间滤波图,并计算每个空间滤波图的权值;其次,计算累加图,并对累加图进行引导图像滤波得到空间引导图;最后,求解基础图像和残差图像,建立基于空间引导滤波的图像细节增强模型对源图像进行图像细节增强。本发明能有效提升对图像细节的增强效果。
The invention discloses an image detail enhancement method based on space-guided filtering, which is characterized by the following steps: firstly, using an image edge detection operator to extract an edge feature response map from a source image, and performing normalization; then, for Create binarized spatial indication maps for different gray-scale intervals, and perform Gaussian convolution on each spatial indication map to obtain a spatial filter map, and calculate the weight of each spatial filter map; secondly, calculate the accumulation map, and Guided image filtering is performed on the accumulation map to obtain a spatially guided map; finally, the basic image and the residual image are solved, and an image detail enhancement model based on spatially guided filtering is established to enhance the image details of the source image. The invention can effectively improve the enhancement effect on image details.
Description
技术领域technical field
本发明涉及图像细节增强方法,更具体地说是一种用于改善图像的视觉效果,加强图像中细节部位图像的判读和识别力的图像细节增强方法。The invention relates to an image detail enhancement method, more specifically, an image detail enhancement method for improving the visual effect of an image and strengthening the interpretation and recognition of detailed images in the image.
背景技术Background technique
21世纪是信息时代,互联网飞速发展,手机、iPad等便携式智能移动设备已经遍布人们日常生活。几乎所有的便携式智能移动设备都具备图像采集功能。全球的用户利用手机或者iPad每天拍摄了大量的图片,并分享到网络上。但是,迫于照片拍摄时所处自然环境因素或者拍摄设备的限制,很多网络图片的视觉效果并不显著。于是,为了提升图片的视觉效果,大量数字图像处理方法被研究者提出。在大量的数字图像处理方法中,图像细节增强方法在近年来受到了学术界和工业界的大量研究者的关注。The 21st century is the information age, with the rapid development of the Internet, portable smart mobile devices such as mobile phones and iPads have spread all over people's daily lives. Almost all portable smart mobile devices have image acquisition functions. Users around the world use mobile phones or iPads to take a large number of pictures every day and share them on the Internet. However, due to the natural environmental factors in which the photos were taken or the limitations of the shooting equipment, the visual effects of many online pictures are not obvious. Therefore, in order to improve the visual effect of pictures, a large number of digital image processing methods have been proposed by researchers. Among a large number of digital image processing methods, image detail enhancement methods have attracted the attention of a large number of researchers in academia and industry in recent years.
图像中细节特征往往包含有重要的信息,尤其是在医学图像方面。然而由于噪声和对比度等因素的影响,这些图像的细节特征的可视性大大降低,不利于对图像细节特征的有效利用。图像细节增强作为一种图像处理方法,不仅能突出图像中的细节特征信息,还能够削弱或者消除干扰信号。The detailed features in images often contain important information, especially in medical images. However, due to the influence of factors such as noise and contrast, the visibility of the detailed features of these images is greatly reduced, which is not conducive to the effective use of image detailed features. As an image processing method, image detail enhancement can not only highlight the detailed feature information in the image, but also weaken or eliminate interference signals.
当前,多个基于边缘特征驱动的图像滤波方法被用于图像细节增强,比如局部拉普拉斯滤波方法和引导图像滤波方法。这类图像滤波方法的主要目标是在没有引入“伪影”的前提下增强图像的细节内容。但是,这类滤波方法在图像滤波过程中对图像所有区域的像素采用统一的滤波强度,没有考虑到滤波强度和不同区域图像内容之间的联系,导致了以下几个缺陷:Currently, several image filtering methods based on edge features are used for image detail enhancement, such as local Laplacian filtering method and guided image filtering method. The main goal of this type of image filtering method is to enhance the details of the image without introducing "artifacts". However, this type of filtering method adopts a uniform filtering strength for pixels in all regions of the image during the image filtering process, without considering the relationship between the filtering strength and the image content of different regions, resulting in the following defects:
(1)在对图像细节进行增强时,“伪影”需要被额外地引入,降低了方法的适用范围;(1) When enhancing image details, "artifacts" need to be additionally introduced, which reduces the scope of application of the method;
(2)这些滤波方法将无法对不同的图像区域采用不同的滤波强度,降低了滤波效果。(2) These filtering methods will not be able to use different filtering strengths for different image regions, reducing the filtering effect.
在图像细节增强过程中,用户一般对图像中某些包含重要视觉信息的区域进行增强,而对其它普通区域则不处理,保持不变。例如,对于一幅包含了“蓝天”、“树”和“山峰”的自然风景照片,大多数用户可能希望对图像中“山峰”或者“树”的区域进行细节增强,而对日常生活中常见的“蓝天”区域保持不变。如果能准确地识别出不同语义的图像区域,进而对不同区域采用不同的滤波强度,图像细节增强的效果将会大大提升。但是,由于低层视觉特征和高层语义内容之间所存在的“语义鸿沟”问题,导致从语义上准确地识别出图像区域变得很困难。因此,当前的图像细节增强方法的增强效果均有限,不能达到用户的要求。In the process of image detail enhancement, the user generally enhances certain areas in the image that contain important visual information, while leaving the other common areas unchanged. For example, for a natural landscape photo containing "blue sky", "tree" and "mountain", most users may wish to enhance the details of the "mountain" or "tree" area in the image, while the common The "blue sky" area remains unchanged. If the image regions with different semantics can be accurately identified, and then different filter strengths are used for different regions, the effect of image detail enhancement will be greatly improved. However, due to the "semantic gap" between low-level visual features and high-level semantic content, it becomes difficult to accurately identify image regions semantically. Therefore, the enhancement effects of the current image detail enhancement methods are limited and cannot meet the requirements of users.
发明内容Contents of the invention
本发明是为避免上述现有技术所存在的不足之处,提供一种基于空间引导滤波的图像细节增强方法,以期有效提升对图像细节的增强效果。The purpose of the present invention is to avoid the shortcomings of the above-mentioned prior art, and to provide an image detail enhancement method based on spatial guidance filtering, in order to effectively improve the effect of image detail enhancement.
本发明为解决方法问题采用如下方法方案:The present invention adopts following method scheme for solving method problem:
本发明一种基于空间引导滤波的图像细节增强方法的特点是按如下步骤进行:A kind of feature of the image detail enhancement method based on spatial guided filtering of the present invention is to carry out as follows:
步骤1,对于分辨率为m×n的源图像I,I∈Rm×n,利用图像边缘检测算子提取所述源图像I的边缘响应图,并将所述边缘响应图除以255进行归一化,获得归一化边缘响应矩阵m为源图像I的长度;n为源图像I的宽度;Step 1. For a source image I with a resolution of m×n, I∈R m×n , use an image edge detection operator to extract the edge response map of the source image I, and divide the edge response map by 255 to perform Normalize to get the normalized edge response matrix m is the length of the source image I; n is the width of the source image I;
步骤2,将所述归一化边缘响应矩阵的灰度范围区间[0,1]均匀划分成k个子区间Ωi,利用式(1)分别对所述k个子区间Ωi中的每个子区间建立相对应的空间指示图M(i),M(i)∈Rm×n,1≤i≤k;Step 2, the normalized marginal response matrix The gray scale interval [0,1] of is evenly divided into k sub-intervals Ω i , and the corresponding spatial indication map M(i) is established for each sub-interval in the k sub-intervals Ω i using formula (1), respectively, M(i)∈R m×n , 1≤i≤k;
式(1)中:(x,y)表示所述归一化边缘响应矩阵中元素的坐标,并对应于所述源图像I中像素的位置;1≤x≤m,1≤y≤n;In formula (1): (x, y) represents the normalized marginal response matrix The coordinates of the elements in and correspond to the positions of the pixels in the source image I; 1≤x≤m, 1≤y≤n;
表示所述归一化边缘响应矩阵中第x行第y列元素的取值位于第i个子区间Ωi中;M(x,y)(i)表示第i个空间指示图M(i)中第x行第y列元素的取值; Denotes the normalized marginal response matrix The values of the elements in row x, column y in the i-th subinterval Ω i ; value;
步骤3、利用高斯卷积依次对每一个空间指示图进行滤波处理,得到k个空间滤波图IGauss(i),IGauss(i)∈Rm×n;Step 3, utilize Gaussian convolution to carry out filter processing to each spatial indicator map in turn, obtain k spatial filter maps I Gauss (i), I Gauss (i)∈R m×n ;
步骤4、依次统计所述归一化边缘响应矩阵中所有元素落在每个子区间中元素的个数,并利用式(2)计算每个空间滤波图的权重;Step 4, counting the normalized marginal response matrix sequentially The number of elements in each subinterval in which all elements fall in, and use formula (2) to calculate the weight of each spatial filter map;
式(2)中,hi表示所述归一化边缘响应矩阵中的元素落在第i个子区间Ωi中元素的个数;w(i)表示落在第i个空间滤波图的权重;In formula (2), h i represents the normalized marginal response matrix The number of elements in the i-th subinterval Ω i ; w(i) represents the weight of the i-th spatial filter graph;
步骤5、利用式(3)计算累加图Sa,Sa∈Rm×n;Step 5, using formula (3) to calculate the cumulative map S a , S a ∈ R m×n ;
步骤6、将所述源图像I作为引导图,采用引导图像滤波方法对所述累加图Sa进行引导图像滤波处理,得到空间引导图S,S∈Rm×n;Step 6, using the source image I as a guide map, using a guide image filtering method to carry out guide image filtering processing on the accumulated map S a to obtain a spatial guide map S, S∈Rm×n ;
步骤7、将所述源图像I作为引导图,采用引导图像滤波方法对所述源图像I自身进行引导图像滤波处理,得到基础图像Ib,Ib∈Rm×n;Step 7, using the source image I as a guide image, and using a guide image filtering method to perform guided image filtering processing on the source image I itself to obtain a basic image I b , I b ∈ R m×n ;
步骤8、利用式(4)所示的图像细节增强模型,对所述源图像I进行图像细节增强处理,获得细节增强图像Io,Io∈Rm×n;Step 8. Using the image detail enhancement model shown in formula (4), perform image detail enhancement processing on the source image I to obtain a detail enhanced image I o , I o ∈ R m×n ;
Io=Ib+S0·S⊙Ir (4)I o =I b +S 0 ·S⊙I r (4)
式(4)中:S0为滤波强度;S0为标量;⊙为哈达玛乘积符号,表示两个矩阵对应相乘;Ir表示残差图像,并有Ir=I-Ib。In formula (4): S 0 is the filter strength; S 0 is a scalar; ⊙ is the Hadamard product symbol, which means that two matrices are multiplied; I r is the residual image, and I r =II b .
与已有技术相比,本发明有益效果体现在:Compared with the prior art, the beneficial effects of the present invention are reflected in:
1、本发明构建了一个空间引导图,可以近似估计图像中不同的语义区域,克服了“语义鸿沟”所带来的图像区域无法识别问题。在图像细节增强时,起到了一个空间引导作用。1. The present invention constructs a spatial guidance map, which can approximately estimate different semantic regions in an image, and overcomes the problem of unrecognizable image regions caused by the "semantic gap". When the image details are enhanced, it plays a role of spatial guidance.
2、本发明提出了一个基于空间引导滤波的图像细节增强方法,将空间引导图与引导图像滤波相结合,形成空间引导图像滤波方法。在空间引导图像滤波过程中,对不同的图像内容区域区别对待,采用不同的滤波强度,克服了原来的引导图像滤波法由于采用统一的滤波强度所带来的缺陷,在图像细节增强时,可以使得增强的图像显得更加自然,视觉效果更明显。2. The present invention proposes an image detail enhancement method based on spatially guided filtering, which combines spatially guided images with guided image filtering to form a spatially guided image filtering method. In the process of space-guided image filtering, different image content areas are treated differently, and different filter strengths are used to overcome the defects of the original guided image filtering method due to the use of uniform filter strength. When image details are enhanced, it can be It makes the enhanced image appear more natural and the visual effect is more obvious.
3、本发明的图像细节增强方法仅仅使用简单的低层视觉特征,因此计算复杂度低,对图像进行处理时速度较快,可以获得良好的用户体验。3. The image detail enhancement method of the present invention only uses simple low-level visual features, so the computational complexity is low, the image processing speed is fast, and good user experience can be obtained.
附图说明Description of drawings
图1为本发明需要进行图像细节增强的源图像;Fig. 1 is the source image that the present invention needs to carry out image detail enhancement;
图2为本发明根据源图像所建立的空间引导图;Fig. 2 is the spatial guide map that the present invention establishes according to the source image;
图3为现有技术中使用统一滤波强度对源图像进行图像细节增强后的图像;FIG. 3 is an image obtained by using a uniform filter strength to enhance image details of a source image in the prior art;
图4为本发明使用基于空间引导滤波的图像细节增强方法对源图像进行图像细节增强后的图像。FIG. 4 is an image after image detail enhancement is performed on a source image using the image detail enhancement method based on spatial guidance filtering in the present invention.
具体实施方式Detailed ways
本实施例中,一种基于空间引导滤波的图像细节增强方法主要用于视觉效果不佳的图片进行图像细节增强,提升图像视觉显著性。该方法可做成软件APP,安装在手机等移动端上或者PC端上。该方法的特点是提出一种空间引导图,并与原有的引导图像滤波方法相结合,形成空间引导图像滤波方法,用于对图像的细节内容进行增强。In this embodiment, an image detail enhancement method based on spatial guidance filtering is mainly used for image detail enhancement of pictures with poor visual effect, so as to improve the visual salience of the image. This method can be made into a software APP and installed on a mobile terminal such as a mobile phone or a PC terminal. The feature of this method is to propose a spatially guided image, and combine it with the original guided image filtering method to form a spatially guided image filtering method, which is used to enhance the details of the image.
本发明方法进行图像细节增强时具体过程如下:The concrete process is as follows when the inventive method carries out image detail enhancement:
步骤1,对于分辨率为m×n的源图像I,I∈Rm×n,利用图像边缘检测算子提取源图像I的边缘响应图,并将边缘响应图除以255进行归一化,获得归一化边缘响应矩阵 m为源图像I的长度;n为源图像I的宽度;Step 1, for the source image I with a resolution of m×n, I∈R m×n , use the image edge detection operator to extract the edge response map of the source image I, and divide the edge response map by 255 for normalization, Obtain the normalized marginal response matrix m is the length of the source image I; n is the width of the source image I;
为了便于介绍,步骤1中源图像是以灰度图像为例进行说明的。如果所要进行细节增强的图像是彩色图像,则对彩色图像中的红、绿、蓝三个颜色通道的图像矩阵分别进行细节增强处理,最后将三个颜色通道的细节增强后的图像矩阵合并为一个完整的彩色图像,即为彩色图像进行细节增强后的图像。图1即为本发明在测试时使用的一幅源图像,主要是一座山峰的图像,但整体的视觉效果不是很好,山峰的细节信息不够丰富。For the convenience of introduction, the source image in step 1 is described using a grayscale image as an example. If the image to be enhanced in detail is a color image, the image matrix of the red, green, and blue color channels in the color image is respectively subjected to detail enhancement processing, and finally the image matrix after detail enhancement of the three color channels is merged into A complete color image, that is, an image after detail enhancement of a color image. Fig. 1 is a source image used in the test of the present invention, mainly an image of a mountain, but the overall visual effect is not very good, and the detailed information of the mountain is not rich enough.
步骤1中的图像边缘检测算子可以为Sobel边缘检测算子或者Laplace边缘检测算子,都是较为经典的边缘检测算子,在matlab软件平台均有相关的函数可以直接调用。图像的边缘区域一般都包含图像细节信息。The image edge detection operator in step 1 can be a Sobel edge detection operator or a Laplace edge detection operator, which are more classic edge detection operators, and there are related functions on the matlab software platform that can be called directly. The edge area of an image generally contains image detail information.
步骤2,将归一化边缘响应矩阵的灰度范围区间[0,1]均匀划分成k个子区间Ωi,利用式(1)分别对k个子区间Ωi中的每个子区间建立相对应的空间指示图M(i),M(i)∈Rm×n,1≤i≤k;Step 2, the normalized marginal response matrix The gray scale interval [0,1] of is evenly divided into k sub-intervals Ω i , using formula (1) to establish a corresponding space indicator map M(i) for each sub-interval in k sub-intervals Ω i , M( i)∈Rm ×n , 1≤i≤k;
式(1)中:(x,y)表示归一化边缘响应矩阵中元素的坐标,并对应于源图像I中像素的位置;1≤x≤m,1≤y≤n;In formula (1): (x, y) represents the normalized marginal response matrix The coordinates of the elements in , and correspond to the position of the pixel in the source image I; 1≤x≤m, 1≤y≤n;
表示归一化边缘响应矩阵中第x行第y列元素的取值位于第i个子区间Ωi中;M(x,y)(i)表示第i个空间指示图M(i)中第x行第y列元素的取值; Represents the normalized marginal response matrix The values of the elements in row x, column y in the i-th subinterval Ω i ; value;
步骤2中空间指示图是一个二值化图,步骤2实际上是在各个灰度层分别对归一化边缘响应矩阵进行二值化。其实,一幅图像中相同的语义的区域一般都处在同一个灰度层,所以空间指示图反映了图像细节内容的空间信息。The spatial indication map in step 2 is a binarized map, and step 2 is actually to normalize the edge response matrix in each gray level Do binarization. In fact, the regions with the same semantics in an image are generally in the same gray level, so the spatial indication map reflects the spatial information of the detailed content of the image.
本发明方法在测试中,k取16。In the test of the method of the present invention, k is 16.
步骤3、利用高斯卷积依次对每一个空间指示图进行滤波处理,得到k个空间滤波图IGauss(i),IGauss(i)∈Rm×n;Step 3, utilize Gaussian convolution to carry out filter processing to each spatial indicator map in turn, obtain k spatial filter maps I Gauss (i), I Gauss (i)∈R m×n ;
实际上,步骤3中高斯卷积法是用连续三次Boxfilter运算进行近似的,目的是在基本保持准确度的前提下降低运算复杂度。In fact, the Gaussian convolution method in step 3 is approximated by three consecutive Boxfilter operations, the purpose of which is to reduce the computational complexity while maintaining the accuracy.
步骤4、依次统计归一化边缘响应矩阵中所有元素落在每个子区间中元素的个数,并利用式(2)计算每个空间滤波图的权重;Step 4. Statistically normalize the marginal response matrix in turn The number of elements in each subinterval in which all elements fall in, and use formula (2) to calculate the weight of each spatial filter map;
式(2)中,hi表示归一化边缘响应矩阵中的元素落在第i个子区间Ωi中元素的个数;w(i)表示第i个空间滤波图的权重;In formula (2), h i represents the normalized marginal response matrix The number of elements in the i-th subinterval Ω i where the elements fall into; w(i) represents the weight of the i-th spatial filter graph;
图像的重要细节信息一般都处在图像中强边缘区域,然而强边缘在整个边缘响应中所占比重较小。图像弱边缘往往在整个边缘响应中占据较大比重,但是弱边缘通常是由噪声形成。考虑到这个现象,式(2)就将比重较小的强边缘分配高权值,比重较大的弱边缘分配低权重。步骤4实际上就是对归一化边缘响应矩阵进行直方图统计,统计的结果转为步骤3中空间滤波图的权值。The important details of the image are generally located in the strong edge area of the image, but the proportion of strong edges in the entire edge response is small. Image weak edges often occupy a large proportion in the entire edge response, but weak edges are usually formed by noise. Considering this phenomenon, formula (2) assigns high weights to strong edges with small proportions, and low weights to weak edges with large proportions. Step 4 is actually the normalized marginal response matrix Perform histogram statistics, and the statistical results are transferred to the weights of the spatial filter graph in step 3.
之前的很多图像细节增强方法对于不同的灰度层都“一视同仁”,采用统一的滤波强度,这导致图像细节增强效果大打折扣。本发明方法在步骤4中进行了“区别对待”。Many previous image detail enhancement methods treat different gray levels equally, and use a uniform filter strength, which leads to a greatly reduced image detail enhancement effect. In step 4, the method of the present invention performs "differential treatment".
步骤5、利用式(3)计算累加图Sa,Sa∈Rm×n;Step 5, using formula (3) to calculate the cumulative map S a , S a ∈ R m×n ;
式(3)将步骤3进行高斯卷积得到的空间滤波图乘以相应的权值进行累加,得到累加图。Equation (3) multiplies the spatial filter map obtained by the Gaussian convolution in step 3 by the corresponding weight and accumulates it to obtain the cumulative map.
步骤6、将源图像I作为引导图,采用引导图像滤波方法对累加图Sa进行引导图像滤波处理,得到空间引导图S,S∈Rm×n;Step 6, using the source image I as a guide map, using the guide image filtering method to carry out guide image filtering processing on the accumulation map S a , to obtain a spatial guide map S, S∈R m×n ;
步骤6中引导图像滤波方法是由微软亚洲研究院视觉计算组的何恺明博士在2010年的欧洲计算机视觉会议上提出的。当源图像为引导图时,引导图像滤波就是一个保持图像边缘的滤波操作。本步骤中,使用引导图像滤波对步骤5中累加图进行滤波,得到最终的空间引导图。空间引导图直接反映了图像不同语义内容的空间信息,为后面的图像细节增强,起到一个“导向”作用。图2就是根据图1所示的源图像所建立的空间引导图。The guided image filtering method in step 6 was proposed by Dr. He Yuming of the Visual Computing Group of Microsoft Research Asia at the European Computer Vision Conference in 2010. When the source image is a guided image, guided image filtering is a filtering operation that preserves the edges of the image. In this step, the accumulated image in step 5 is filtered by guided image filtering to obtain the final spatial guided image. The spatial guidance map directly reflects the spatial information of different semantic content of the image, and plays a "guiding" role for enhancing the details of the subsequent image. Figure 2 is the spatial guidance map created based on the source image shown in Figure 1 .
步骤7、将源图像I作为引导图,采用引导图像滤波方法对源图像I自身进行引导图像滤波处理,得到基础图像Ib,Ib∈Rm×n;Step 7. Using the source image I as a guide map, use the guide image filtering method to perform guide image filtering on the source image I itself to obtain the basic image I b , I b ∈ R m×n ;
本步骤中基础图像Ib反映了低频段的基础图像内容。The basic image I b in this step reflects the content of the basic image in the low frequency band.
步骤8、利用式(4)所示的图像细节增强模型,对源图像I进行图像细节增强处理,获得细节增强图像Io,Io∈Rm×n;Step 8. Using the image detail enhancement model shown in formula (4), perform image detail enhancement processing on the source image I to obtain a detail enhanced image I o , I o ∈ R m×n ;
Io=Ib+S0·S⊙Ir (4)I o =I b +S 0 ·S⊙I r (4)
式(4)中:S0为滤波强度,S0为标量,⊙为哈达玛乘积符号,表示两个矩阵对应相乘;Ir表示残差图像,并有Ir=I-Ib。本发明方法在测试时,滤波强度S0取3。残差图像Ir反映了高频段的图像细节内容。In formula (4): S 0 is the filter strength, S 0 is a scalar, ⊙ is the Hadamard product symbol, which means that two matrices are multiplied correspondingly; I r means the residual image, and I r =II b . When the method of the present invention is tested, the filter strength S0 is set to 3. The residual image I r reflects the image details in the high frequency band.
式(4)反映的是本发明提出的基于空间引导滤波的图像细节增强模型。该模型实际上是式(5)所示的图像细节模型的改进:Equation (4) reflects the image detail enhancement model based on spatial guided filtering proposed by the present invention. This model is actually an improvement of the image detail model shown in Equation (5):
Io=Ib+S0·Ir (5)I o =I b +S 0 ·I r (5)
可以明显地看出式(5)的图像细节增强模型是采用的统一的滤波强度,而式(4)的图像细节增强模型相比式(5))多出一个空间引导图S,而本发明提出的空间引导图S正是考虑图像内容与滤波强度之间的关系,针对不同图像内容区域而采取了不同的滤波强度。It can be clearly seen that the image detail enhancement model of formula (5) adopts a unified filter strength, while the image detail enhancement model of formula (4) has one more spatial guide map S than formula (5), and the present invention The proposed spatial guidance map S just considers the relationship between image content and filtering strength, and adopts different filtering strengths for different image content regions.
图3正是根据式(5)所示的图像细节增强模型对图1所示的源图像进行细节增强后的图像,可以看出图3不仅“山峰”和“树木”区域的图像细节得到增强,而且“天空”区域的图像也得到了增强,这部分区域是不应该得到增强,增强后,图像整体显得很突兀,这就是使用统一滤波强度所带来的弊端。Figure 3 is exactly the image after detail enhancement of the source image shown in Figure 1 according to the image detail enhancement model shown in formula (5). It can be seen that not only the image details of the "mountain" and "tree" areas are enhanced in Figure 3 , and the image in the "sky" area has also been enhanced. This part of the area should not be enhanced. After the enhancement, the image as a whole looks very abrupt. This is the disadvantage of using uniform filter strength.
图4是使用式(4)所示的基于空间引导滤波的图像细节增强模型对图1所示的源图像进行增强后的图像。相比图3,图4在山峰和树木区域进行了增强,而对“天空”区域没有进行增强,这正是想要得到的图像增强结果。Fig. 4 is an image after the source image shown in Fig. 1 is enhanced using the image detail enhancement model based on spatial guided filtering shown in formula (4). Compared with Figure 3, Figure 4 has enhanced the mountain and tree areas, but not the "sky" area, which is exactly the desired image enhancement result.
以上,仅为本发明较佳的一种实施方式,任何熟悉本技术领域的技术人员在本发明揭露的技术范围内,根据本发明的技术方案及其发明构思加以等同替换或相关参数改变,都应涵盖在本发明的保护范围之内。The above is only a preferred implementation mode of the present invention. Anyone familiar with the technical field within the technical scope disclosed in the present invention, according to the technical solution of the present invention and its inventive concept to make equivalent replacements or related parameter changes, all Should be covered within the protection scope of the present invention.
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