将以下 翻译成英语 急急~~属于“机械类专业术语”
图像降噪算法，在下问了下学过这个的同仁，勉强了解了点大概
尽力翻译了，希望对楼主有所帮助
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为了清楚起见，将第一部分分开放置
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Firstly, we begin with a test function to investigate the edge preserving capability of the proposed denoising algorithm. Filtered results appear in Fig.1. The chosen test function has some abrupt changing features such as discontinuities or sharp bumps. We corrupted the test function by addition simulated spatially Gaussian noise with mean 0 and standard deviation 0.02 shown in Fig.1(a). Fig.1(b) shows the filtered results by local Wiener filter in spatial domain and Fig1.(c) shows the result of the proposed approach. As it appears, Fig.1(b) shows that step edges are poorly filtered by local Wiener filter in spatial domain and the ability of the proposed approach to smooth homogeneous regions simultaneously preserving edges is better than local Wiener filter in spatial domain.
Secondly, the standard 256×256 Lena image is presented for additive Gaussian noise. We corrupted it by addition Gaussian noise at different noise levels. The proposed approach is also compared to other techniques. For quantitative evaluation, we use PSNR (peak signal-to-noise ratio) to evaluate the performance of denoising algorithm. Table 1 lists the results of the PSNR improvement of the proposed algorithm compared with other methods-standard LS-SVM, median filter and wiener filter. It is evident from Table 1 that the proposed method consistently outperforms standard LS-SVM and median filter method in terms of PSNR improvement. Fig.2 shows the comparison of the denoised Lena image corrupted with Gaussian noise at PSNR=22.09 dB. As it appears, Fig.2(b) shows that step edges are poorly filtered by standard LS-SVM filter. The median filter and Wiener filter can reduce noise respectively in certain range. Fig.2(e) shows the ability of our approach to smooth homogeneous regions simultaneously preserving edges. Especially, details of the image are very obvious, e.g, eyes and hat. This indicates that the proposed denoising method achieved the most successful noise reduced in statistically homogeneous areas and preserved interesting edge details.
4.Conclusions
We highlighted an efficient approach of nonlinear Wiener filtering that relies on the theory of least support squares vector regression. With the proposed approach, the clean signal and random noise are first mapped from input space into a high-dimensional feature space by a suitable nonlinear mapping ∅ and then local Wiener filter is considered in this feature space . This technology provides an elegant way of dealing with the problem of nonlinear Wiener filter by reducing them to linear ones in some feature space nonlinearly related to input space. Furthermore, the number of kernel function satisfying the Mercer condition is enormous. One can also use the wavelet kernel or other kernels as kernel functions to remove noise. Thus, the freedom of the present new registration is very large. Experimental results show that the algorithm can preserve edges with better visual quality and yield better results.
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翻译如下
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第一，我们由一个测试函数开始，来研究（前文）提出的降噪算法的保边能力。滤波结果如图1所示。所选取的测试函数有一些突变的特征，比如一些不连续点和一些尖锐凸点。我们通过附加如图1（a）所示的均值为0，标准差为0.02的模拟空间高斯噪音，来劣化测试函数。图1（b）显示了通过在空间域里的局部维纳滤波法滤波后的结果，图1（c）显示了通过（前文）提出的方法来滤波的结果。正如上图所示，图1（b）显示出用空间域里的局部维纳滤波法来滤波对阶梯边缘保边效果很差，而（前文）提出的算法，相较空间域中的局部维纳滤波法，对多个平滑均调区域进行同时保边能力有较好表现。
第二，为了显现加性高斯噪声，（我们先）出示一张标准256×256的Lena图。我们通过不同噪音级别的加性高斯噪声来劣化这张图，然后也用（前文）提出的算法同其他技术来比较。为了进行量化评估，我们使用峰值信噪比（PSNR）来评价降噪算法的表现。表1列出了和其他算法--标准最小二乘支持向量机（LS-SVM），中值滤波法以及维纳滤波法--相比较，（前文）提出的算法对峰值信噪比的优化结果。从表1我们显然可以看出该方法在峰值信噪比优化上稳稳胜过标准最小二乘支持向量机（LS-SVM）和中值滤波法。图2显示峰值信噪比PSNR=22.09dB时用高斯噪音劣化的Lena图（用不同算法）降噪后的比较。图2（b）显示了标准最小二乘支持向量机（LS-SVM）滤波法对阶梯边缘保边效果很差。中值滤波法和维纳滤波法分别能够对特定的范围进行降噪。图2（e）显示了我们的方法对平滑均调区域进行同时保边能力。尤其，图像的细节很清晰易见，如眼睛和帽子。这说明我们提出的降噪算法在统计学上均调的区域上取得了最成功的降噪效果，保存了有意义的边缘细节。
4.结论
我们这里要强调一个基于最小二乘向量回归理论的高效的非线性维纳滤波法。用我们提出的算法，清晰信号和随机噪音首先通过一个合适的非线性对应法则∅从输入空间被映射到高维特征空间，然后在这个特征空间里考虑使用局部维纳滤波法。这个技术，通过把在一些同输入空间非线性相关的特征空间里的清晰信号和随机噪音简化成线性的，从而优美地解决了非线性维纳滤波法的问题。另外，满足Mercer条件的核函数的数目巨大。也可以用小波核或者其他的核作为核函数来除噪。因此，现在新的注册登记的自由度非常大。实验结果表明该算法能够以更好的图像质量保边，给予更佳的结果。
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以上
Glossary（按出现顺序）
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1.edge preserving/preserve edge 保护锐边，或者保边
2.denoising algorithm 降噪算法
3.Filter 原意为“刷选”，这里是“滤波”的意思
4.spatially Gaussian noise 空间高斯噪音
5.Wiener filter 维纳滤波法
6.PSNR (peak signal-to-noise ratio) 峰值信噪比
7.LS-SVM （least square-support vector machine）最小二乘支持向量机
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希望对楼主有所帮助吧
看到楼上的机器翻译不怎么对劲，给你人工翻译一下吧：
This paper mainly introduces the four-wheel positioning structure, the principle and the discussion of application in maintenance. There are some ways of measuring wheel alignment and fast fault diagnosis of the fixing position of an automobile as well as the methods of the four-wheel alignment application, mainly based on various vehicle types as examples to study.
料位计：Anticipate to account:
重锤料位计：The heavy hammer anticipate to account:
尤梯尔：
爱尔美特：Love Er the United States especially:
科隆：Section Long:
威力巴:Ba of the power:
Level meter:
Level of a heavy hammer:
Util:
Ireland U.S. special:
Cologne:
Weiliba:
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