Image magnification method using joint diffusion
Image magnification method using joint diffusion
复制标题
使用联合扩散的图像放大方法
DOI：
10.1007/bf02945597
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发表时间：
2004-09
期刊：
影响因子：
--
通讯作者：
S.L.Peng
中科院分区：
文献类型：
--
作者：
Z.X.Liu;J.Wang;S.L.Peng
本文提出了一种新的图像放大算法。由于线性放大/内插技术降低了对比度并产生锯齿效应，近年来人们提出了许多非线性内插方法，特别是基于非线性扩散的方法来解决这些问题。最近提出的两种扩散内插技术，前向和后向扩散(FAB)和水平集重建(LSR)，不能同时增强对比度和平滑边缘。本文提出了一种基于偏微分方程组(PDE)的新方法。本文的贡献包括：首先，构造了一种融合FAB和LSR的统一扩散模型；其次，为了消除联合扩散的伪影，用软约束代替了LSR提出的硬约束；第三，讨论了联合系数的确定、停止时间的判定和彩色图像的处理。结果表明，该方法在视觉和定量上均优于双三次曲面、FAB和LSR。
In this paper a new algorithm for image magnification is presented. Because linear magnification/interpolation techniques diminish the contrast and produce sawtooth effects, in recent years, many nonlinear interpolation methods, especially nonlinear diffusion based approaches, have been proposed to solve these problems. Two recently proposed techniques for interpolation by diffusion, forward and backward diffusion (FAB) and level-set reconstruction (LSR), cannot enhance the contrast and smooth edges simultaneously. In this article, a novel Partial Differential Equations (PDE) based approach is presented. The contributions of the paper include: firstly, a unified form of diffusion joining FAB and LSR is constructed to have all of their virtues; secondly, to eliminate artifacts of the joint diffusion, soft constraint takes the place of hard constraint presented by LSR; thirdly, the determination of joint coefficients, criterion for stopping time and color image processing are also discussed. The results demonstrate that the method is visually and quantitatively better than Bicubic, FAB and LSR.
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影响因子：
2.9
作者：
ALVAREZ, L;MAZORRA, L
通讯作者：
MAZORRA, L
DOI：
10.1109/icassp.1995.479971
发表时间：
1995-05
期刊：
1995 International Conference on Acoustics, Speech, and Signal Processing
影响因子：
--
作者：
S. G. Chang;Z. Cvetković;M. Vetterli
通讯作者：
S. G. Chang;Z. Cvetković;M. Vetterli
DOI：
10.1109/tassp.1978.1163154
发表时间：
1978-12
期刊：
IEEE Transactions on Acoustics, Speech, and Signal Processing
影响因子：
--
作者：
H. Hou;H. Andrews
通讯作者：
H. Hou;H. Andrews
影响因子：
10.6
作者：
Gilboa, G;Sochen, N;Zeevi, YY
通讯作者：
Zeevi, YY
DOI：
10.1109/cvpr.2000.854852
发表时间：
2000-06
期刊：
Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)
影响因子：
--
作者：
Simon Baker;Takeo Kanade
通讯作者：
Simon Baker;Takeo Kanade