Description: This MEX performs 2d bilinear interpolation using an NVIDIA graphics chipset. To compile and run this software, one needs the NVIDIA CUDA Toolkit (http://www.nvidia.com/object/cuda_get.html) and, of course, an NVIDIA graphics card of reasonably modern vintage.
BUILDING INSTRUCTIONS: Change the 'MATLAB' (and if necessary, 'MEX') variables in the Makefile to appropriate values, then simply run 'make' at a prompt and an executable (mex/mexmac/mexmaci/dll?) file will be created.
This code uses your GPU's built-in bilinear texture interpolation capability, and is very fast. For reasonably sized operations (taking, say, a 50x50 matrix up to 1000x1000) CUDA-based code is 5-10x faster than linear interp2 (as tested on a MBP 2.4GHz C2D, GeForce 8600M GT).
With very (VERY) large matrices, however, it has the capability of completely crashing your computer or giving bizarre results. Be careful!
Platform: |
Size: 37881 |
Author:whitewalter |
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Description: 2D图像的线性插值拉伸用于图像的增强处理-2D image of the linear interpolation for image enhancement of tensile deal Platform: |
Size: 91136 |
Author:caolly |
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Description: 信道估计仿真，采用了多种方法：线性插值，MMSE，1惟MMSE,2维MMSE等-Channel Estimation
1 for non-block-wise linear interpolation,
2 for block-wise 1-D MMSE,
3 for non-block-wise 1-D MMSE,
4 for block-wise 2-D MMSE,
5 for non-block-wise 2-D MMSE,
6 for ideal channel estimator,
7 for random pilots map case using 2-D MMSE. Platform: |
Size: 4096 |
Author:todd |
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Description: interpolates平面图像Z坐标点与习近平,易。 Z是假定被定义在定期间隔1点:1:M,N,N][M,地方=大小(Z)。
如果兮,易建联价值观是图像边界,把外面的NaNs在《紫。
Matlab函数是相似的土著
INTERP2字(Z =、烯、彝族、“线性”、南),
但多的主人。如果Z是矢量阵二维图像三维价值),然后反复讨论,interpolates:Z(:1)、Z(,,)、2)::::Z(,,,3),. .等。这部作品对每个图像interpolate速度比独立,例如
字,(1)::,Z(= INTERP2(1):、:、、烯,易建联)
字,(2):= INTERP2:,(Z(::,2),习近平,易建联)
字,(3)::,Z(= INTERP2,(3)::、、烯,易建联)
额外的速度增益系数从precomputation插值的,这是相同的所有图像。一套图像插值是有用的,如用于图像配准的,当一个人必须interpolate形象和梯度矢量价值或如RGB图像。
局限性:只工作对于图像在一个规则的网格,并且只有在线性插值方法。
出门的时候,可别忘了编译第一:
mirt2D_mexinterp.cpp mex
享受吧!多见于我的主页。
https://sites.google.com/site/myronenko/
- ZI = mirt2D_mexinterp(Z,XI,YI) interpolates 2D image Z at the points with coordinates XI,YI. Z is assumed to be defined at regular spaced points 1:N, 1:M, where [M,N]=size(Z).
If XI,YI values are outside the image boundaries, put NaNs in ZI.
The function is similar to Matlab s native
ZI = INTERP2(Z,XI,YI, linear ,NaN),
but is much master. If Z is vector valued 2D image (3D array), then iteratively interpolates Z(:,:,1), Z(:,:,2),Z(:,:,3),.. etc. This works faster than to interpolate each image independently, such as
ZI(:,:,1) = INTERP2(Z(:,:,1),XI,YI)
ZI(:,:,2) = INTERP2(Z(:,:,2),XI,YI)
ZI(:,:,3) = INTERP2(Z(:,:,3),XI,YI)
The extra speed gain is from the precomputation of coefficients for interpolation, which are the same for all images. Interpolation of a set of images is useful, e.g. for image registration, when one has to interpolate image and its gradients or for vector valued images such as RGB.
Limitations: only works for images on a regular grid, Platform: |
Size: 4096 |
Author:abeaqhax |
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Description: In this paper, we propose a novel algorithm for image inpainting based on compactly supported
radial basis functions (CSRBF) interpolation. The algorithm converts 2D image inpainting problem
into implict surface reconstruction problem from 3D points set. Firstly, we construct the implicit
surface for approximating the points set which convert from damaged image by using radial basis
functions (RBF), and then resample from the constructed surface can evaluate the pixels’ value of
damaged or removed portion on the image. Using compactly supported radial basis functions, the
matrix of corresponding system of the linear algebraic equations is spare and bounded. So it can
decrease the computational complexity of RBF algorithm. Experiments show that good results are
obtained by using the proposed algorithm. -In this paper, we propose a novel algorithm for image inpainting based on compactly supported
radial basis functions (CSRBF) interpolation. The algorithm converts 2D image inpainting problem
into implict surface reconstruction problem from 3D points set. Firstly, we construct the implicit
surface for approximating the points set which convert from damaged image by using radial basis
functions (RBF), and then resample from the constructed surface can evaluate the pixels’ value of
damaged or removed portion on the image. Using compactly supported radial basis functions, the
matrix of corresponding system of the linear algebraic equations is spare and bounded. So it can
decrease the computational complexity of RBF algorithm. Experiments show that good results are
obtained by using the proposed algorithm. Platform: |
Size: 274432 |
Author:payal |
Hits:
Search - 2D linear interpolation