英语翻译However,about 25% provides a reasonable preservation.It is worthynoting that NEVPM has lower neighborhood preservation.Onereason is that neighborhood preservation depends on the low-frequencycomponent,whereas in NEVPM,the high-frequency i

英语翻译However,about 25% provides a reasonable preservation.It is worthynoting that NEVPM has lower neighborhood preservation.Onereason is that neighborhood preservation depends on the low-frequencycomponent,whereas in NEVPM,the high-frequency i
英语翻译
However,about 25% provides a reasonable preservation.It is worthy
noting that NEVPM has lower neighborhood preservation.One
reason is that neighborhood preservation depends on the low-frequency
component,whereas in NEVPM,the high-frequency is assumed
to be conditional independent of the low-frequency
component given the mid-frequency one.
In NEVPM,patch validation is introduced for noise removal (Wei
and Yeung,2007).Here,we also combine the technique with SRNE
using the proposed feature 13a1 for a comprehensive comparison.
As we can see,in all the experiments on the twelve images,consistent
results are achieved.Because of the limitation of space,for the
visual comparisons,only the results of the image labelled 1 are
presented as an illustrative example in Figs.6 and 7 (more examples
in Supplementary material).While only first-order gradient
feature (denoted by 1) and the feature used in SRNE (denoted by
12) yield results with obvious jagged staircase effect in the constant
color region and NEVPM yields results with noisy edges and
blur,the proposed improved feature combination with a (13al)
preserves sharper edges and shapes and suppress blocky artifacts
such as the edges of goblet and book.We also give the quantitative
analysis on the root mean squared error (RMSE) which has the following
format:
where ^yi stands for the values of pixel in the ideal target Y and yi
stands for the values of corresponding pixels in output Yt .And n
stands for the number of total pixels in Y.
It indicates that the improved feature combination yields visually
best results and best quantitative analysis (lowest RMSE) under
the same learning and training condition.
哥哥姐姐们 认真点啊 不要用金山快译啊 我要用的 软件我也会啊

英语翻译However,about 25% provides a reasonable preservation.It is worthynoting that NEVPM has lower neighborhood preservation.Onereason is that neighborhood preservation depends on the low-frequencycomponent,whereas in NEVPM,the high-frequency i
However,about 25% provides a reasonable preservation.It is worthy noting that NEVPM has lower neighborhood preservation.One reason is that neighborhood preservation depends on the low-frequency component,whereas in NEVPM,the high-frequency is assumed to be conditional independent of the low-frequency component given the mid-frequency one.
然而,大约有25%提供了一个合理的预留.对NEVPM具有较低的邻域预留来说这是个有价值的算法.理由是邻域预留依赖于低分辨的构成组件,但是在NEVPM中,高分辨是被假定为有条件地独立于低分辨组件的,而这些低分辨组件是由某个中分辨图象给出的.
In NEVPM,patch validation is introduced for noise removal (Wei and Yeung,2007).Here,we also combine the technique with SRNE using the proposed feature 13a1 for a comprehensive comparison.
在NEVPM中,确认碎片被介绍成为是反干扰(Wei和Yeung,2007年).这里,我们也利用被提议的特征13a1来结合带有SRNE的技术做个全面的比较.
As we can see,in all the experiments on the twelve images,consistent results are achieved.Because of the limitation of space,for the visual comparisons,only the results of the image labelled 1 are presented as an illustrative example in Figs.6 and 7 (more examples in Supplementary material).
如我们所见,在12个图象的所有实验中,我们得到了一致的结果.由于空间所限,对于视觉对比的结果,我们只将图象1作为说明性的例子在图示6和图示7中呈现出来（更多例子见补充材料）.
While only first-order gradient feature (denoted by 1) and the feature used in SRNE (denoted by 12) yield results with obvious jagged staircase effect in the constant color region and NEVPM yields results with noisy edges and blur,the proposed improved feature combination with a (13al) preserves sharper edges and shapes and suppress blocky artifacts such as the edges of goblet and book.
仅当第一顺序梯度特征（表示1）和用于在连续色彩区域中其结果明显带有锯齿状阶梯形的SRNE的特征（表示12）,以及用于结果带有边缘和污点干扰的NEVPM的特征,被提议的改进的特征组合a（13al）保留了较锋利的边缘和外形以及人为控制的块状,比如酒杯和书的边缘.
We also give the quantitative analysis on the root mean squared error (RMSE) which has the following format:where ^yi stands for the values of pixel in the ideal target Y and yi stands for the values of corresponding pixels in output Yt .
我们也基于平均方根误差(RMSE) 给出量化分析,它具有如下形式：其中^yi代表理想目标Y中的象素值,而yi代表输出量Yt中的相应象素值.
And n stands for the number of total pixels in Y.It indicates that the improved feature combination yields visually best results and best quantitative analysis (lowest RMSE) under the same learning and training condition.
而n代表Y中的总象素值.这表明改进的特征组合产生最佳视觉效果以及基于相同学习和训练条件下的最佳定量分析（RMSE最低）.
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