AnimatePicture

这个代码是基于论文“Saliency Detection via Graph-Based Manifold Ranking”下的程序源码，作者是Chuan Yang, Lihe Zhang, Huchuan Lu, Ming-Hsuan Yang, and Xiang Ruan 功能1：使用canny算子计算图像的二值边缘图 (The code is for paper "Saliency Detection via Graph-Based Manifold Ranking" by Chuan Yang, Lihe Zhang, Huchuan Lu, Ming-Hsuan Yang, and Xiang Ruan Procedures: 1. compute a binary edge map of the image using the canny method. )

利用pcnn和nsct实现图像融合，非下采样Contourlet变换，脉冲耦合神经网络，连接强度(Using NSCT and PCNN image fusion, the sampling Contourlet transform, the pulse coupled neural network, the connection strength)

用Matlab产生正弦波,矩形波,以及白噪声信号，并显示各自时域波形图 2．进行FFT变换，显示各自频谱图，其中采样率，频率、数据长度自选 3．做出上述三种信号的均方根图谱,功率图谱,以及对数均方根图谱 4．用IFFT傅立叶反变换恢复信号，并显示恢复的正弦信号时域波形图 (Using Matlab generated sine wave, rectangular wave, as well as the white noise signal, and display their respective time-domain waveform of Figure 2. FFT to transform, showing their frequency spectrum, including sampling rate, frequency, data length of 3-on-demand. Make the rms signal of the three maps, power maps, as well as the number of root-mean-square map 4. Fourier Transform IFFT with the restoration of signals, and displays the sinusoidal signal the resumption of time-domain waveform)

对各种不同的传感器测得数据进行融合处理，从而获得精确的环境信息，能够做出更正确的判断。这个是一位非常优秀的老师的课件，非常值得一看！是关于数据融合方面的(On a variety of sensor data fusion measured to obtain accurate environmental information to make more accurate judgments. This is a very good teacher of the courseware, is worth a visit! Data fusion)

类似PChunter的内核工具。本工具是应用层的源码。适合内核新手(Similar PChunter core tools. This tool is the source application layer. Suitable for novice kernel)

说明：  图像去雾基于暗原色先验去雾，何凯明算法的源代码改进，(Dark colors on the image to fog prior to fog, He Kaiming algorithm source code improvements,)

使用 visual c++编写的基本的数字图像处理程序，(The use of visual c++ Prepared basic digital image processing procedures,)

用霍夫曼编码，零树小波算法解码，图像处理过程(huffman encode ，ezw decode)

应用kmean均值聚类方法对RGB,HSR图像的分割算法，个人觉得自己变得还可以，希望大家借鉴。(application kmean means clustering method of RGB, neighborhood image segmentation algorithm, individuals feel that they can become the hope that you learn from.)

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.)