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PF
说明： 经典的粒子滤波算法，适用于粒子滤波的初学者更好的了解粒子滤波(Classical particle filter, particle filter for better understanding of particle filter for beginners)
- 2011-11-29 09:39:53下载
- 积分：1
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R2007test_walsh
说明： 采用walsh进行处理，所得图片效果比较理想(Processed by walsh, results from the ideal image)
- 2011-03-08 09:39:08下载
- 积分：1
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iradonclj
平行光CT重建，频域滤波反投影iradon代码(FBP iradon)
- 2016-10-21 16:24:21下载
- 积分：1
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anigaussm
去除高斯噪声的有效码源，去噪能力强，使简单。(The effective removal of Gaussian noise source code, denoising capability, so that simple.)
- 2008-04-01 17:55:27下载
- 积分：1
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Watershed
说明： 对图像进行标记分水岭算法分割，有效的避免过分割(Marked watershed algorithm on the image segmentation, effectively avoid over-segmentation)
- 2010-04-15 19:05:42下载
- 积分：1
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28_REVIEW
Image enhancement is a processing on an image in order to make it more appropriate for certain applications.
It is used to improve the visual effects and the clarity of image or to make the original image more conducive for
computer to process. Contrast enhancement changing the pixels intensity of the input image to utilize maximum
possible bins. We need to study and review the different image contrast enhancement techniques because contrast
- 2016-04-22 20:49:34下载
- 积分：1
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matlab-Face-Recognition-Detection
MATLAB环境下编的人脸检测和识别算法，人脸识别部分采用的pca算法，另外包括一个人机界面，直接运行facedec程序即可运行(MATLAB environment, compiled by face detection and recognition algorithms, face recognition pca algorithm is used in some of the other, including a man-machine interface, programs can be run directly run the facedec)
- 2009-12-07 21:48:40下载
- 积分：1
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OMP
说明： 压缩感知稀疏恢复中使用的OMP算法，matlab例程(OMP algorithm and MATLAB routine used in compressed sensing sparse recovery)
- 2019-04-09 09:20:25下载
- 积分：1
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kmeans1
基于K-means的优化聚类中心的图像分割算法，改进了FCM算法的缺点(Based on the optimization of K-means clustering center image segmentation algorithm to improve the shortcomings of the FCM algorithm )
- 2017-05-03 15:12:46下载
- 积分：1
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SparseLab200-Core
基于多帧图像插值（Interpolation）技术的方法是SR恢复技术当中最直观
的方法。这类方法首先估计各帧图像之间的相对运动信息，获得HR图像在非均
匀间距采样点上的象素值，接着通过非均匀插值得到HR栅格上的象素值，最后
采用图像恢复技术来去除模糊和降低噪声（运动估计!非均匀插值!去模糊和
噪声）。(In this paper, we propose a novel method for solv-
ing single-image super-resolution problems. Given a
low-resolution image as input, we recover its high-
resolution counterpart using a set of training exam-
ples. While this formulation resembles other learning-
based methods for super-resolution, our method has
been inspired by recent manifold learning methods, par-
ticularly locally linear embedding (LLE). Speci?cally,
small image patches in the low- and high-resolution
images form manifolds with similar local geometry in
two distinct feature spaces. As in LLE, local geometry
is characterized by how a feature vector correspond-
ing to a patch can be reconstructed by its neighbors
in the feature space. Besides using the training image
pairs to estimate the high-resolution embedding, we
also enforce local compatibility and smoothness con-
straints between patches in the target high-resolution
image through overlapping. Experiments show that our
method is very ?exible )
- 2010-11-07 11:15:03下载
- 积分：1
