英语翻译In this experiment,we want to evaluate the influence of featurecombination to the quality of recovered high-resolution imagewhen using the neighbor embedding algorithm.According to theviewpoint of Freeman et al.(2002),we also attempt to a

英语翻译In this experiment,we want to evaluate the influence of featurecombination to the quality of recovered high-resolution imagewhen using the neighbor embedding algorithm.According to theviewpoint of Freeman et al.(2002),we also attempt to a
英语翻译
In this experiment,we want to evaluate the influence of feature
combination to the quality of recovered high-resolution image
when using the neighbor embedding algorithm.According to the
viewpoint of Freeman et al.(2002),we also attempt to approximate
a reasonable weighting factor between matching the local
information (i.e.the norm luminance) and finding compatible adjacent
information (i.e.the first-order gradient feature).The distance
measure between two patches pi and pj is defined as
where DistGrad1 and DistNormL stand for the Euclidean distances of
first-order gradients and normalized luminance between pi and pj,
respectively.a,which is the weighting factor between the two feature
vectors,is approximately set to be 4.The reasoning details are
included in Supplementary material and the setting is verified
empirically as shown in the experiments.
In order to find out the contribution of the feature combination
independently,we don’t apply any other proposed improvements
in the experiments for this part.Instead we adopt the exact procedure
and parameters of the SRNE algorithm which originally employs
first- and second-order gradient concatenation as feature to
do the following three evaluations

英语翻译In this experiment,we want to evaluate the influence of featurecombination to the quality of recovered high-resolution imagewhen using the neighbor embedding algorithm.According to theviewpoint of Freeman et al.(2002),we also attempt to a
在这个试验中,当我们使用邻域嵌入算法的时候,我们想要结合质量和特征点评估这种算法对高分辨图象的影响.
根据弗里曼等人（ 2002年）的观点,我们也尝试接近一个在本地信息（即规范亮度）和寻找相容的临近信息（即一阶梯度特征）之间的合理的加权因子.在pi和pj这两种小块之间距离的措施被定义为DistGrad1和DistNormL
分别代表在pi和pj之间Euclidean的一阶梯度距离和pi和pj之间的规范化亮度.
这两个特征向量的权重因子被近似的设为4.推理的详细内容在补充材料里面而且这个假设也在多次实验所显示的结果证明.
为了独立的找出相结合的特征的贡献,我们在这部分实验中没有应用其他的改进意见.取而代之的是应用了精确的程序和SRNE算法原先使用
一阶和二阶梯度结合得出的参数作为特征值
做出的以下三个评价
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