Description: 基于分块的人脸图像超分辨率学习算法实验平台，利用学习到的训练集图像信息对低分辨率图像超分辨率，并可以查看超分辨率后的图像块匹配结果（用鼠标双击结果显示窗口中的实验图像），可以设置实验参数。-Block-based face image super-resolution algorithm for the experimental platform to learn, learning to use the training set of low-resolution image information super-resolution images and can view the image super-resolution block-matching results (double-click with the mouse results display window of the experimental images), the experimental parameters can be set. Platform: |
Size: 2290688 |
Author:menchael |
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Description: 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 Platform: |
Size: 27595776 |
Author:qianyeyu |
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Description: 自适应超分辨率图像盲重建算法研究与应用
清华大学综合训练论文-Blind adaptive super-resolution image reconstruction algorithm and application of comprehensive training thesis of Tsinghua University Platform: |
Size: 6009856 |
Author:cherry |
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Description: Support vector regression has been proposed in a number of image processing tasks including blind
image deconvolution, image denoising and single frame super-resolution. As for other machine learning
methods, the training is slow. In this paper, we attempt to address this issue by reducing the feature
dimensionality through Principal Component Analysis (PCA). Our single frame supper-resolution
experiments show that PCA successfully Platform: |
Size: 239616 |
Author:sanjay |
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Description: Support vector regression has been proposed in a number of image processing tasks including blind
image deconvolution, image denoising and single frame super-resolution. As for other machine learning
methods, the training is slow. In this paper, we attempt to address this issue by reducing the feature
dimensionality through Principal Component Analysis (PCA). Our single frame supper-resolution
experiments show that PCA successfully Platform: |
Size: 497664 |
Author:sanjay |
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Description: Support vector regression has been proposed in a number of image processing tasks including blind
image deconvolution, image denoising and single frame super-resolution. As for other machine learning
methods, the training is slow. In this paper, we attempt to address this issue by reducing the feature
dimensionality through Principal Component Analysis (PCA). Our single frame supper-resolution
experiments show that PCA successfully Platform: |
Size: 250880 |
Author:sanjay |
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Description: Support vector regression has been proposed in a number of image processing tasks including blind
image deconvolution, image denoising and single frame super-resolution. As for other machine learning
methods, the training is slow. In this paper, we attempt to address this issue by reducing the feature
dimensionality through Principal Component Analysis (PCA). Our single frame supper-resolution
experiments show that PCA successfully Platform: |
Size: 212992 |
Author:sanjay |
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Description: Support vector regression has been proposed in a number of image processing tasks including blind
image deconvolution, image denoising and single frame super-resolution. As for other machine learning
methods, the training is slow. In this paper, we attempt to address this issue by reducing the feature
dimensionality through Principal Component Analysis (PCA). Our single frame supper-resolution
experiments show that PCA successfully Platform: |
Size: 458752 |
Author:sanjay |
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Description: Support vector regression has been proposed in a number of image processing tasks including blind
image deconvolution, image denoising and single frame super-resolution. As for other machine learning
methods, the training is slow. In this paper, we attempt to address this issue by reducing the feature
dimensionality through Principal Component Analysis (PCA). Our single frame supper-resolution
experiments show that PCA successfully Platform: |
Size: 218112 |
Author:sanjay |
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Description: Support vector regression has been proposed in a number of image processing tasks including blind
image deconvolution, image denoising and single frame super-resolution. As for other machine learning
methods, the training is slow. In this paper, we attempt to address this issue by reducing the feature
dimensionality through Principal Component Analysis (PCA). Our single frame supper-resolution
experiments show that PCA successfully Platform: |
Size: 489472 |
Author:sanjay |
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Description: romp算法实现基于稀疏表示的图像超分辨率算法字典对的训练，本代码为matlab文件 -Romp algorithm to realize image super-resolution algorithm based on sparse representation of a dictionary of training, the code for the matlab files
Platform: |
Size: 1024 |
Author:文玉强 |
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Description: 该代码是基于学习的超分辨率重建，此算法是在线字典学习，字典的训练与稀疏表示与重建同时进行。-This code is based on the study of super-resolution reconstruction, this algorithm is learning online dictionaries, dictionaries of training and sparse representation and reconstruction simultaneously. Platform: |
Size: 217088 |
Author:李丹 |
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Description: 超分辨卷积神经网络，深度学习，MATLAB代码，获取深度学习的训练数据-Super-resolution convolution neural network, the depth of learning, MATLAB code, get deep learning of the training data Platform: |
Size: 19618816 |
Author:cdq481 |
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Description: 稀疏重建联合字典训练用于超分辨率重建，基于正则化框架-Dictionary sparse reconstruction joint training for super-resolution reconstruction, based on the regular framework Platform: |
Size: 1024 |
Author:chen zhanhong |
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Description: 通过卷积神经网络CNN实现超分辨率重建，利用训练模型实现参数和权重偏移的训练，达到输入低分辨率图像，输出高分辨率图像，测试例程附录。-Convolution neural network CNN to achieve super-resolution reconstruction using a training model parameters and to achieve weight shift training to achieve a low-resolution image input and output high-resolution image, test routine appendix. Platform: |
Size: 3072 |
Author:ll |
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Description: 滤波器训练的超分辨率算法，以实现快速图像复原(Super-resolution algorithm for filter training to achieve fast image restoration) Platform: |
Size: 45056 |
Author:shirleyChang
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Description: 实现了图像基于学习的超分辨率重构，输入训练图片和待处理图片，得出超分辨率处理后的图像(Super-resolution reconstruction based on learning, Input the training picture and the image to be processed to get the image after super-resolution processing) Platform: |
Size: 2853888 |
Author:Mark_Z
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Search - super resolution training