问题描述：
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
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
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