搜索资源列表
MSSIM
- Source code of the MSSIM (Mean Structure Similitary Index). MSSIM is a measure of distortion static images. It s comparing distorted image with reference image and as the result return value between 0 and 1. The quality criteria is one of the most cl
normalise
- Normalises image values to 0-1, or to desired mean and variance Usage: n = normalise(im) Offsets and rescales image so that the minimum value is 0 and the maximum value is 1. Result is returned in n. If the image is colour the
strip111
- 简单的去条带方法，采用基于IDL的HSI图像条带噪声去除方案研究中的方法2，该算法为某个象元值 * 该影像全部象元的均值 / 该象元所在列的列均值。-Simple method to strip, IDL-based HSI image strip with noise removal program study 2, the algorithm is a pixel value where* images all like the element of the mean/pixel colum
kmeans(cp2Bp2B)
- kmeans聚类算法实现图像分割， 基于K-MEAN的图像分割,方便实用，对于图像处理的研究生很有参考价值的！-kmeans clustering algorithm for image segmentation, image segmentation based on K-MEAN, convenient and practical, for image processing graduate of great reference value!
minimum-spanning-tree
- 用快速最小生成树使用k-均值方法进行医学图像分割-Medical image segmentation using k- mean value method for fast minimum spanning tree
image-fusion13
- 这是从网上整理出来的图像融合评价标准，总共有13项性能指标。包括平均梯度，边缘强度，信息熵，灰度均值，标准差(均方差MSE)，均方根误差，峰值信噪比（psnr），空间频率(sf)，图像清晰度，互信息(mi)，结构相似性(ssim)，交叉熵(cross entropy)，相对标准差。-This is sorted out the online image fusion uation criteria, there are a total of 13 performance indicators
carnum
- 1. 分量法。 2.最大值法。选取彩色图像中的三分量中（RGB）的颜色的最大值作为灰度图的灰度值。即：f(i,j)=max(R(i,j)，G（i,j）,B(i,j))。 3.平均值法。 将彩色图像中的三分量亮度求平均得到一个灰度图f(i,j)=(R(i,j)+G(i,j)+B(i,j))/3。 4.加权平均法。(The 1. component method. 2. maximum value method. The maximum value of the c
Harris
- 基于离散分数布朗随机场模型的水下图像目标检测方法。该方法根据分形理论和水下图像的特点，以图像中每 个像素点为中心取窗口，计算在该窗口内的分形维数均值，将该均值作为中心像素的分形特征，然后根据分形维 数分布图确定分割阈值，从而实现对水下图像分割，并且通过将目标表面不同尺度下的灰度差分平均值进行归一 化处理，减少了用于表示不同尺度下的平均绝对值灰度差分的数据，从而提高算法检测效率(Underwater target detection method based on discrete frac
ASR fusion
- This package contains the code which is associated with the following papers: Yu Liu, Zengfu Wang, "Simultaneous image fusion and denoising with adaptive sparse representation". IET Image Processing,vol.9, no.5 ,pp.347-357, 2015 Edite