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双语推荐：图像增强

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由于传统的图像增强算法得到的增强图像存在细节的缺失，主观效果较差等缺陷，提出了一种图像增强算法。通过Retinex模型保证了增强图像具有较突出的细节特性；通过求解泊松方程满足了增强图像与原始图像在梯度域的一致性；采用自适应亮度映射得到适于显示的边界条件；对区域的边界进行采样降低算法的复杂度。实验对比了几种图像增强算法得到的增强图像以及相关的评价系数，验证了该算法能够有效地提高图像的对比度，增强图像的主观视觉效果较高。
As the traditional image enhancement is suffered from uncomfortable details and visual effect, this paper proposes a new algorithm. The details are ensured by using Retinex model. The consistency in gradient domain between original image and restoration is guaranteed by solving the Poission equation. It adopts the adaptive luminance method to obtain the Dirichlet boundary condition. To reduce the complexity, boundary sampling method is executed. By making comparison with restorations, it shows that this algorithm effectively improves contrast, meanwhile the restorations represent excellent effect of sense.

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本文研究的重点是各种水下图像增强算法。首先,从图像增强的基本理论出发,详细介绍了目前几种主要的图像增强技术。其次,阐述了水下光学特性以及常规图像增强技术对水下图像处理,并分析其适用性。最后,针对光照不均匀的水下图像增强技术,分析其实用性和局限性,并利用Retinex算法对水下图像达到增强的效果。
This paper focuses on the study of image enhancement algorithms .Firstly ,proceeding from the basic theory of image enhancement ,detailing the current main image enhancement technology .Secondly ,elabo‐rated on the underwater optical characteristics and general technology on underwater image enhancement .Ana‐lyze their applicability .Finally ,analyze its relevance and limitations about underwater images enhancement technology for uneven illumination ,and use the Retinex algorithm on underwater images to achieve enhanced effect .

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为充分利用图像目标像素点周围8个方向及其邻近像素点的信息,构造一种新的分数阶微分图像增强模板,并根据分数阶微分的Riemann-Liouville定义,推导出模板系数。利用该模板及其它分数阶微分图像增强模板,分别对灰度图像和彩色图像进行图像增强处理实验;引入图像熵的概念和计算公式,对图像增强结果进行熵值比较。实验及熵值结果表明,该分数阶微分图像增强模板能有效保留图像的纹理细节和边缘信息,图像增强效果更为明显且熵值最大。
To make full use of the image pixels ,which including the eight directions and the neighborhood around the image cen-tral pixel ,a kind of new image enhancement mask based on the fractional differential was constructed ,and the mask coefficients were based on the Riemann-Liouville definition of the fractional differential .Gray images and color images were experimented using the new and other fractional differential image enhancement masks .At last ,the definition of the image entropy and its cal-culation formula were introduced ,and the computing results of the image entropy were compared and analyzed .The results of image enhancement experiments and the image entropy calculation show that the new fractional differential mask can maintain the image texture feature and the image boundary information effectively .The image enhancement effect is better than other masks and the image entropy is the best .

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为了更好地辨别低照度图像中的目标，提高图像的对比度并增强纹理细节信息，采用子层分割规定化算法对图像进行增强处理，提出一种结合子层分割和自适应函数引导的规定化图像增强算法。首先根据原始图像直方图的特点对直方图进行子层分割，之后通过对分割后的子层进行灰度区间拉伸来提高图像的亮度，以增强图像的对比度；为了增强图像中的细节信息，在每个子层求取自适应函数来对每个子层做规定化处理；最后对子层直方图合并，得到增强后图像。实验结果证明，经过增强后图像的灰度平均梯度值为原始图像的3～4倍，信息熵也明显增大；该算法在增强图像细节、提高图像的对比度上具有优越性。
In order to distinguish the target of the illumination image better 、increase the contract and enhance the weak information ,by using sub‐layer segmentation and histogram specification to enhance image , we propose the image enhancement combined sub‐layer segmentation with histogram specification induced by adaptive function algorithm .Firstly ,according to the features of the original image ,its histogram will be divided into sub‐layer .Then ,stretching gradation interval of the sub‐layer to increase the image brightness ,the image contrast can be enhanced . To enhance the image details better ,we use the required adaptation functions to do the histogram specification in each sub‐layer .Finally ,the sub‐layer histogram will be merged to get the enhanced image .Experimental results show that the gray mean gradient value of the enhanced image was 3‐4 times of the original image ,and entropy was increased obviously .The algorithm can enhance image detail and improve ima

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在图像处理过程中,使用直方图均衡化、全局对比度拉伸等基于图像全局信息的图像增强算法,难以实现图像局部细节对比度的增强,即不能实现物体轮廓的增强。为此,本文主要对基于局部直方图均衡化、Lee算法的图像局部增强算法和MSR算法进行研究,通过算法的改进来实现图像局部增强的效果。
In the process of image processing, using the histogram equalization, the global contrast stretching the image en-hancement algorithm based on global information , it is difficult to achieve the image local contrast enhancement , that is cannot a-chieve the object contour one.Therefore, in this paper our research on local enhancement algorithm and MSR algorithm is based on the main image local histogram equalization, Lee algorithm.By the improvement of the algorithm, we get the local image enhance-ment effect.

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由于医学X光图像中存在信噪比低、清晰度差、对比度低等缺点,而普通的图像增强算法很难在增强图像细节特征的同时对图像的背景噪声进行抑制。针对上述问题,将模糊多尺度Retinex算法引入医学图像处理的增强算法中:先计算图像中每个像素点的模糊度,再结合模糊度采用多尺度Retinex算法对图像进行增强。实验表明该算法比直方图均衡、同态滤波以及普通的多尺度Retinex算法增强图像的效果更明显,并能在增强图像的同时,有效抑制图像的背景噪声。
The medical X-ray radiography images always have the character of low Signal-Noise Ratio ( SNR) ,bad definition and con-trast,and it is difficult to reduce the noise while enhancing the detail characters by the custom image enhancement algorithm. According to the problems mentioned above,Fuzzy Multi-Scale Retinex ( FMSR) algorithm was applied to medical image enhancement. Firstly the fuzzy degree of each pixel was calculated,then the MSR algorithm was applied combined with fuzzy degree to enhance the image. Experi-ment shows that compared with the custom image enhancement algorithm as histogram equalization,homomorphic filtering and the cus-tom MSR algorithm,this algorithm can get a better performance,and the noise can be efficiently reduced while enhancing the image.

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为了提高图像处理效果，提出一种改进MSR图像增强算法。首先将MSR算法中的对数函数改为双曲正切函数，然后采用MSR算法对图像进行增强处理，以提高图像的增强质量。采用仿真实验对改进MSR算法与其它图像增强算法进行对比。采用具体仿真实验对改进MSR算法的性能进行验证。仿真结果表明，相对于其它图像增强算法，改进MSR算法明显提高了图像增强后的质量，具有良好的视觉欣赏效果。
In order to improve the effect of image processing, this paper proposes an improved MSR image enhancement algorithm. the logarithm function in the MSR algorithm is replace by hyperbolic tangent function, and then the MSR algorithm is used to enhance image processing to improve the quality of image enhancement. The simulation experiments is carried out to test the performance of the improved MSR a. The simulation results show that the proposed algorithm significantly improves the quality of image enhancement compared with other image enhancement algorithm, it has good visual effect of the appreciation.

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图像增强是大多数图像处理技术的前提工作,对于彩色图像,既要达到图像增强的目的又要保持色调不变成为一个难点。在研究RGB颜色空间和HSV颜色空间特点、数学形态学图像处理技术的基础上,在HSV颜色空间把一种灰度图像对比度增强算法推广到彩色图像处理算法。实验结果表明,该算法在局部自适应增强图像对比度的同时,能够保持图像色调,提高图像质量。
Most of the image processing techniques are based on the image enhancement operation, but the dodgy problem in the enhancement of color images is to enhance images without changing their original hue. The present research aims to introduce one of the solutions to this problem. Based on the study of the characteristics of RGB color space, the HSV color space, and mathematical morphology image processing technique, this article presents a solution to the problem by extending grey-level image contrast enhancement algorithm to the color image in HSV color space. The experiment results show that this algorithm can keep the original hue and enhance the quality of images while maintaining the adaptive contrast enhancement in local parts.

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为了有效增强含有噪声点的图像,采用基于自适应平滑的图像增强方法。首先根据当前像素点与周围相邻像素点灰度之差决定是将此点进行均值滤波还是沿着某方向进行加权滤波,即利用多个平滑模板对图像进行自适应平滑滤波得到平滑后的图像;然后将原图像减去平滑后的图像得到细节图像;最后将细节图像乘以系数后加在平滑后的图像上得到增强的图像。与单纯的平滑、单纯的锐化相比,此方法不仅有效去除了噪声,又增强了图像的细节,增强效果明显。
To enhance the noise image,the image enhancement method based on self-adaptive smooth filtering is adopted in this paper. Firstly,according to the gray difference of the current pixel and the adjacent pixel,it decides whether the average filtering or weighted filtering should be adopted to get the smoothed image,that is,several smooth templates are utilized to per-form the self-adaptive smooth filtering of the image to obtain smoothed image. Secondly,the detailed image is got by detracting the smoothed image from the original image. Last,the enhanced image is achieved by adding the detailed image multiplied by the coefficient to the smoothed image. Compared with the methods which enhance the image by smoothing or sharping only,the method can not only remove noise from images ,but also enhance the details of images obviously.

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传统的图像增强算法在增强图像时，存在丢失细节和增大噪声以及运行时间长等问题，为了满足冲压工件缺陷自动在线检测技术的需要，本文提出了基于Contourlet变换和混沌小生境粒子群优化算法（NCP-SO）相融合的图像增强算法。首先，对图像进行Contourlet变换分解，在带通方向子带进行自适应增强来实现对工件图像的增强；然后将NCPSO算法引入，克服了Contourlet变换在图像增强时速度较慢等问题，提高了算法效率。通过实验表明，该方法在增强效果和运算时间上都优于传统的图像增强算法。
When enhance image by Traditional image enhancement algorithms, there are problems such as de-tails are lost and increase noise and long operation time, in order to meet the needs of the stamping workpiece defect automatic on-line inspection technology, Contourlet transform and NCPSO fusion image enhancement algorithm is proposed. First of all, the image decomposed by Contourlet transform, in the direction of band pass the adaptive en-hancement is used to realize the workpiece image enhancement;and then introduce NCPSO algorithm, to overcome the Contourlet transform need long time, which improve the efficiency of the algorithm. Experiments show that this method is superior to the traditional image enhancement algorithms on the enhancement effect and the operation time.

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