说明 ：1、以下表述都是从各论文收集而来，并非原创；
2、由于引文较多，不一一列出，在此对所有作者表示感谢；如有侵权，请联
系，核实后删除！
3、 由于是持续更新，所以每部分的内容没有排序，可能会有些乱，各位请见
谅！
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摘要：
算法描述：
实验设置说明：
1、说明参数如何设置：
For the sake of simplicity we consider here a linear function of the initial noise level, from a= 0 for b= 0 to a= 0.9 for b= 50.
2、客观评价参数说明（PSNR和SSIM）：
We evaluate our denoising results with two image quality measures: the popular Peak Signal to Noise Ratio (PSNR)and the Structural Similarity Index (SSIM) . While simple and practical, the PSNR relies only on the absolute difference pixel by pixel, and does not provide a good signal fidelity measure . As such, its ability to compare images from a human perception point of view is poor. The SSIM is somehow a more complete image quality measure, which builds upon the idea that human perception is highly adaptive to structural information from images and visual scenes 。
实验结果说明：
1、说明自己的算法整体要优于比较算法，但是不是所有的情况：
in all cases, significant improvements have been made in this objective measure, and the proposed algorithm generally (but nExperimental results justify the performance of the proposed learning-based up-sampling scheme,
which significantly outperforms the state-of-the-art up-sampling algorithms in
terms of PSNR (more than 1 dB improvement), M-SSIM and subjective quality. ot always) provides superior PSNR results relative to the other two algorithms.
2、说明自己算法在客观指标以及主观效果都要好
Experimental results justify the performance of the proposed learning-based up-sampling scheme, which significantlyoutperforms the state-of-the-art up-sampling algorithms in terms of PSNR (more than 1 dB improvement), M-SSIM and subjective quality.
总结：
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作者简介： 在读研究生，专注于图像处理技术研究
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