基于小波变换的图像融合源代码

base papers of matlab data mining in image processing base papers

利用该函数可以对图像进行仿射变换，是不错的matlab源代码(Function can make use of the image affine transformation, is a good source code matlab)

视频编解码技术，在有限带宽的情况下，能够大幅度的压缩编码比特数，图像的质量没有丝毫的下降。(Video codec technology, in the case of limited bandwidth can be substantial coding bits, the image quality is not the slightest decline.)

图像处理作业：基于高频补偿算法实现图像滤波及分析 数值分析作业：四种方法实现求解根号3的值(Image processing operations: image filtering and analysis of numerical analysis operations: four methods based on high-frequency compensation algorithm to solve the root of the value)

基于matlab的手势识别代码，有gui编写界面(Gesture recognition code based on matlab, GUI programming interface)

本程序和实现对一幅包含人脸的bmp图像进行光纤补偿，对人脸区域进行检测实现人眼和人嘴的检测与定位，最后勾绘出人脸的特征以实现人脸检测的目的。(This procedure and implementation of a compensate the fiber contains face of BMP image, the face region detection to realize human eyes and mouth detection and positioning, finally drew out the facial features to achieve the purpose of face detection. )

压缩感知compressed sensing ,剑桥大学 教授，可以用于降噪、图像处理、文件压缩等等；(Cambridge- Compressive Sensing- Theory and Application；)

二值图像处理运算是从数学形态学下的集合论方法发展起来的,尽管它的基本运算很简单，但是却可以产生复杂的效果。常用的二值图像处理操作有许多方法，如腐蚀、膨胀、细化、开运算和闭运算等等。本文对这些内容作些研究探讨， 希望对爱好图像处理的朋友有所帮助。(Binary image processing algorithms are developed set theory in mathematical morphology. Although its basic operation is simple, it can produce complex effects. Commonly used binary image processing operations there are many ways, such as corrosion, expansion, refinement, open operation and closed operation and so on. In this paper, some of these studies to explore, I hope to love image processing friends help.)

最新图像轮廓透明度计算算法。 发表在今年的计算机视觉会议(latest image contours transparency calculation algorithm. Published in this year's Computer Vision Conference)

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 )