Median-filter

非常适合计算机视觉方面的研究使用,包括四元数的各种计算,算法优化非常好，几乎没有循环。( Very suitable for the study using computer vision, Including quaternion various calculations, Algorithm optimization is very good, almost no circulation.)

通过小波变换方法对雾天的图像去雾，实现图像增强的效果(Through the wavelet transform of images of fog to fog, to achieve the effect of image enhancement)

基于NSCT变换的自适应去噪，里面包含仿真图(image denoising)

最经典的弗洛伊德加网算法.图像半色调处理，在印刷纺织等相关领域进行图像的加网计算，而已实现二值化(halftone )

SIFT，即尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点，是一种局部特征描述子。[1] 该方法于1999年由David Lowe[2] 首先发表于计算机视觉国际会议（International Conference on Computer Vision，ICCV），2004年再次经David Lowe整理完善后发表于International journal of computer vision（IJCV）。截止2014年8月，该论文单篇被引次数达25000余次。(SIFT, namely Scale Invariant Feature Transform (Scale-invariant feature transform, SIFT), it is a description for image processing. This description has scale invariance, can detect critical point in the image, it is a local feature descriptor. [1] The method in 1999 by David Lowe [2] First published in the International Conference on Computer Vision (International Conference on Computer Vision, ICCV), 2004 was again perfect finishing after David Lowe published in the International journal of computer vision (IJCV) . As of August 2014, the paper cited the number of times a single article of more than 25,000 times.)

图像增强图像增强 对比度增强 视觉增强对比度增强 视觉增强(image Enhanceme)

说明：  计算图像对应的各阶原点矩，中心矩，以及Hu不变量,输入是图像和矩的阶数。(Image raw，Central，and Hu s moments computation)

这是文章Nonlocally Centralized Sparse Representation for SR的代码(this is the code of Nonlocally Centralized Sparse Representation for SR)

案例推理,用于匹配,欢迎大家交流,有资源共享,(Case-based reasoning for matching, welcomed the U.S. exchange, resources sharing,)

一种基于特征点的图像匹配算法 一种基于特征点的图像匹配算法(Feature point based image matching algorithm based on feature points matching algorithm)