renyanjiance

这是清华大学章毓敏写的图像工程，分上中下三册，希望对学习图像处理的学者有所帮助。(This is the engineering of Tsinghua University Zhang Yumin, write the image points on the three, hoping to help scholars learn image processing.)

demons算法用来实现图像的配准，特别是医学图像的配准，已经达到了很高的精度(demons algorithm is used to achieve image registration, especially in medical image registration, has reached a very high accuracy)

In the use of MATLAB to complete the wavelet transform and threshold to the process of image compression, very good

tiff图像的读取与处理，并且说明清新，有很大的帮助。(The reading and processing of the tiff image, and the description of the fresh, is of great help.)

对视频生成sprite图像，费了作者很大的力气，拿来与大家共享！(sprite generation of video images, and a lot of effort author, used to share with you!)

通过图片的外参数矩阵对图片进行透视变换，对图片的外参数进行校正(camera calibration)

高斯马尔可夫随机场纹理分割程序matlab(a gaussian markov random field for texture segmentation)

利用傅立叶变换的图像同态滤波，解决光照不均的问题(Use of Fourier transform images homomorphic filtering to solve the problem of uneven illumination)

在已知高度的情况下利用单目视觉摄像头来进行定位。(Positioning with a monocular vision camera at a given height)

图像修复是对图像中破损区域进行信息填充，以减少图像破损所带来的信息损失的过程。 传统的图像修复方法需要依赖图像的具体结构来制定相应的修复方法，压缩感知理论的提出，使得可以利 用信号的稀疏性来对图像进行修复。基于K 奇异值分解（KSVD）与形态学成分分析（MCA，Morphological Component Analysis）的图像修复方法首先采用形态学成分分析方法对破损图像进行特征分析，将其分解 为结构部分和纹理部分；然后基于学习型字典KSVD分别对这两部分进行过完备字典训练；最后利用训练得 到的字典实现对破损图像的修复。相比于传统的图像修复方法，该方法具有适应性强、修复效果好等优点(Image inpainting is to fill the missing data in corrupted images and thus to reduce the information loss of damaged image. Traditional inpainting algorithms are dependent on specific structure of target images compressive sensing theory makes is possible to realized image inpainting with signal sparsity. This paper proposes a novel inpainting algorithm based on KSVD and MCA algorithm, which first decomposes the image into texture part and structure part, and then trains the two dictionaries for these two parts with KSVD and reconstructs the original image with these two trained dictionaries. Experiment indicates that the proposed algorithm is of better adaptability and performance as compared with traditional algorithms.)