Super-resolution via adaptive combination of color channels
Super-resolution via adaptive combination of color channels
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DOI：
10.1007/s11042-015-3124-1
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发表时间：
2015
影响因子：
3.6
通讯作者：
Jian Xu;Z. Chang;Jiu-lun Fan;Xiaoqiang Zhao;Xiaomin Wu;Yanzi Wang;Xiaodan Zhang
中科院分区：
文献类型：
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作者：
Jian Xu;Z. Chang;Jiu-lun Fan;Xiaoqiang Zhao;Xiaomin Wu;Yanzi Wang;Xiaodan Zhang
超分辨率技术是根据一幅或一组观测到的低分辨率图像重建出清晰的高分辨率图像的技术。然而，许多现有方法需要“工具”的帮助，这导致训练和存储这些“工具”的时间和内存成本很高。本文提出了一种SR方法，可以执行这些培训“工具”。该方法包括两个阶段：颜色通道自适应组合和正则化。在第一阶段中，颜色通道自适应组合将由不同颜色通道捕获的纹理组装到亮度分量中。该策略有助于有效利用不同波段的亮度信息。在第二阶段，提出了一种改进的全变分（TV）正则化方法，以抑制伪影和锐化边缘。TV正则化在迭代公式中增加了一个新项，使边缘与所需的高分辨率图像相似。接下来，使用迭代反投影将高分辨率图像拟合到所观察到的低分辨率图像。实验结果表明，该算法优于现有的许多基于学习的方法，具有较低的时间成本是上级。
Super-resolution (SR) is a technology to reconstruct a clear high-resolution image with plausible details according to one or a group of observed low- resolution images. However, many existing methods require the help of “tools”, which results in high time and memory costs for training and storing these “tools”. This paper proposes an SR method that can be executed without these training “tools”. This method comprises two stages: color channel adaptive combination and regularization. In the first stage, color channel adaptive combination assembles the textures captured by different color channels into the luminance component. This strategy is helpful in effectively utilizing the luminance information of different light bands. In the second stage, an improved total variation (TV) regularization method is proposed to suppress artifacts and sharpen edges. The TV regularization adds a new item in the iteration formula to enable the edges to be similar to the desired high-resolution image. Next, iterative back projection is used to fit the high-resolution image to the observed low-resolution image. The experimental results demonstrate that the proposed algorithm is superior to many existing learning-based methods and has low time cost.