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基于非负相邻嵌入的低复杂度单图像超分辨率
Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding |
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| 课程网址: |
http://videolectures.net/bmvc2012_bevilacqua_neighbor_embedding/
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| 主讲教师: |
Marco Bevilacqua
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| 开课单位: |
法国国家信息与自动化研究所
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| 开课时间: |
2012-10-09
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| 课程语种: |
英语
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| 中文简介: |
本文介绍了一种基于非负邻域嵌入的单图像超分辨率（SR）算法。它属于基于示例的单图像SR算法家族，因为它使用低分辨率（LR）和高分辨率（HR）训练的补丁对字典来推断未知的HR细节。输入图像中的每一个lr特征向量在字典中表示为其k个最近邻的加权组合，在保持局部lr嵌入的前提下重建相应的hr特征向量。为了建立一个低复杂度的竞争算法，引入了三个关键方面：（i）补丁的紧凑而有效的表示（特征表示）（i i）补丁最近邻的精确估计（权重计算）（i i i）一个紧凑且已构建（因此是外部）的字典，它允许电子步进放大。所设计的邻域嵌入SR算法与其他最先进的方法相比，具有良好的视觉效果，同时也大大缩短了计算时间。
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| 课程简介: |
This paper describes a single-image super-resolution (SR) algorithm based on nonnegative neighbor embedding. It belongs to the family of single-image example-based SR algorithms, since it uses a dictionary of low resolution (LR) and high resolution (HR) trained patch pairs to infer the unknown HR details. Each LR feature vector in the input image is expressed as the weighted combination of its K nearest neighbors in the dictionary; the corresponding HR feature vector is reconstructed under the assumption that the local LR embedding is preserved. Three key aspects are introduced in order to build a low-complexity competitive algorithm: (i) a compact but efficient representation of the patches (feature representation) (ii) an accurate estimation of the patches by their nearest neighbors (weight computation) (iii) a compact and already built (therefore external) dictionary, which allows a one-step upscaling. The neighbor embedding SR algorithm so designed is shown to give good visual results, comparable to other state-of-the-art methods, while presenting an appreciable reduction of the computational time.
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| 关 键 词: |
单图像超分辨率; 权重计算; 分辨率; 视觉效果
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-04-07：chenxin
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| 阅读次数: |
65
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