搜索资源列表
Shock-Filters
- 利用Shock Filters去除图像噪声，附件里有关于Shock Filters的网页介绍-use Shock Filters remove image noise, annex on the Shock's website on Filters
ransac.rar
- 用VC++实现了ransac算法，可以用于图像匹配时去除错误匹配等，代码是可用的。,Using VC++ implementation of the RANSAC algorithm, image matching can be used to remove the error when the match and so on, the code are available.
Leefilter
- Lee滤波器，具有很好的去噪效果，结合各种小波，可以有效去除SAR图像噪声。-Lee filter, has a good de-noising effect of combination of wavelet can effectively remove the SAR image noise.
BD IP2006
- 一个盲去卷积程序，可以实现去除图像的各类模糊，同时可进行相关参数的自适应调整。-A blind deconvolution process can be achieved to remove all kinds of image blur, while the relevant parameters can be adaptive.
image-process
- 使用opencv对图像进行了简单的去除噪声等处理。希望对学习opencv的朋友有帮助-Opencv image using a simple processing to remove noise. Hope of learning opencv friends help
AveEnhance
- 实现图像的均值滤波，去除图像噪声使图像看起来更平滑，模板窗口大小3*3,5*5,9*9。-Image of the mean filter, remove image noise to make the image look smoother, the Template window size 3* 3,5* 5,9* 9.
Wiener
- 用维纳滤波去除图象噪声,保持图象清晰可见.-Wiener filter used to remove image noise, maintaining the image clearly visible.
Object_Removal_by_Exemplar-Based_Inpainting
- 基于样例的图像修补方法，可以用于移除较大的目标，修复较大的区域。VC++环境，Qiushuang Zhang设计，参见A.Criminisi等人的论文：Object Removal by Exemplar-Based Inpainting.-Based on the sample image repair method can be used to remove larger goal, to repair the larger region. VC++ Environment, Qiushua
denoise
- 图像去躁函数，FIR窗函数去除噪点-Image to impatient function, FIR window function to remove noise
medianFilter
- 图像处理中的中值滤波源代码，能够较好的模糊图像，除去椒盐噪声。-Image processing median filter source code, be able to better fuzzy images, remove the salt and pepper noise.
im_MSfilter
- 基于Mean Shift的图像分割过程就是首先利用Mean Shift算法对图像中的像素进行聚类，即把收敛到同一点的起始点归为一类，然后把这一类的标号赋给这些起始点，同时把包含像素点太少的类去掉。然后，采用阈值化分割的方法对图像进行二值化处理 -Mean Shift Based on the process of image segmentation is the first to use the image Mean Shift algorithm for clustering of pixe
ImageDeNosieBaseOnPcnn
- 根据PCNN工作原理和噪声特点提出了一种改进的PCNN去噪方法，能够有效去除椒盐噪声，并且很好地保留图像细节。-According to working principle and the noise characteristics of PCNN proposed an improved PCNN de-noising method can effectively remove the salt and pepper noise, and keep a good image detail.
DeNosingBaseOnXiaoBo
- 一种改进的基于PCNN神经网络和QPSO粒子行为的PSO的图像滤波算法，也可以较好地去除高斯噪声-Improved PCNN-based neural network and PSO behavior QPSO particle image filtering algorithm can also be better to remove Gaussian noise
guasssmooth
- 图像平滑用于去除图像中的噪声。高斯平滑，就是将每个像素的灰度值用其领域的加权平均值代替。该算法简单，能够有效去除高斯噪声。 -Image Smoothing is used to remove image noise. Gaussian smoothing is the gray value of each pixel with its weighted average of the field instead. The algorithm is simple and can effecti
11
- 基于阈值自适应的曲波图像去噪算法，实验结果表叫，它较原有算法更好地实现去除噪声与保护图像细节之间的平衡。-Threshold-based adaptive curvelet image denoising algorithm, called the results table, it better than the original algorithm to remove noise and to achieve a balance between preserving image detail
MATLAB_Image_Processing
- MATLAB进行图像处理的一些实用程序，有图像增强 ，图像复原的，还有神经网路的，还有一些实例，比如形态学实例——从PCB图像中删除所有电流线，仅保留芯片对象 。内附说明，希望能够对正在进行数字图像处理学习的人有帮助-MATLAB image processing some of the practical procedures, image enhancement, image restoration, as well as neural networks, there are some in
Perona-Malik
- 本算法使用图像处理中的PM算法去除图像噪声-The algorithm uses image processing algorithms to remove image noise PM
Bilateral-Filtering
- 提出利用双边滤波方法去除图像噪声与保持边缘，非常经典的一篇文献，做图像去噪，重建等工作的都可以参考。-Proposed the use of bilateral filtering to remove image noise and to keep the edge of a very classic literature, do image denoising, reconstruction work can refer to.
Image-processing-experiments
- Image processing experiments基本的图像处理实验。图形界面，实现了水平翻转,垂直翻转,图像旋转,灰度图像,单阈值二值化,双阈值二值化,非0置白法,孤立像素清除,3*3平均值滤波,N*N中值滤波,一阶微分,二阶微分,拉普拉斯变换,负像,缩小,放大,原始大小-Image processing experiments basic image processing experiments. Graphical interface to achieve a horizontal
Algorithm-for-Sequence-Image-Automatic-Mosaic-bas
- Abstract—Constraining by cameras’ view-angles of the outdoor monitoring systems, the panoramic digital images fail to be obtained directly from photographing. A method is proposed on the basis of the scale invariance feature transform (i.