asimpledpcmcode

编程实现二维8*8图像块的DFT，DCT变换 输入一副RGB图像，将其转化为YCbCr颜色空间，然后对Y分量做分块DFT变换，保存为灰度图像，统计计算时间 编程实现8*8块DCT变换,然后分别显示，当使用64个DCT系数里面的一些系数进行重构，比较其质量；同时熟悉采用FFT来实现DCT变换的思想，并显示2维8*8DCT变换的基函数图像(Programming the DFT, DCT transform of two dimensional 8*8 image block Input a pair of RGB images, transform it into YCbCr color space, then divide the Y component into block DFT transform, save as gray image, and calculate the calculation time The 8*8 block DCT transform is implemented by programming, and then it is shown that when using some coefficients in 64 DCT coefficients, the quality is reconstructed. Meanwhile, the idea of DCT transformation is familiar with FFT, and the base function image of the 2 dimensional 8*8DCT transform is displayed.)

数字图像处理和图像识别的vc++编程方法(Digital image processing and image recognition of the vc++ Programming)

说明：  ORB-SLAM 基本延续了 PTAM 的算法框架,但对框架中的大部分组件都做了改进, 归纳起来主要有 4 点: ORB-SLAM 选用了 ORB 特征, 基于 ORB 描述量的特征匹配和重定位, 都比 PTAM 具有更好的视角不变性。此外, 新增三维点的特征匹配效率更高, 因此能更及时地扩展场景。扩展场景及时与否决定了后续帧是否能稳定跟踪。ORBSLAM 加入了循环回路的检测和闭合机制, 以消除误差累积。系统采用与重定位相同的方法来检测回路(匹配回路两侧关键帧上的公共点), 通过方位图 (Pose Graph) 优化来闭合回路。PTAM 需要用户指定 2 帧来初始化系统, 2 帧间既要有足够的公共点, 又要有足够的平移量. 平移运动为这些公共点提供视差 (Parallax) , 只有足够的视差才能三角化出精确的三维位置。ORB-SLAM 通过检测视差来自动选择初始化的 2 帧。PTAM 扩展场景时也要求新加入的关键帧提供足够的视差, 导致场景往往难以扩展. ORB-SLAM 采用一种更鲁棒的关键帧和三维点的选择机制——先用宽松的判断条件尽可能及时地加入新的关键帧和三维点, 以保证后续帧的鲁棒跟踪; 再用严格的判断条件删除冗余的关键帧和不稳定的三维点，以保证 BA 的效率和精度。(Orb-slam basically continues the algorithm framework of PTAM, but most of the components in the framework have been improved, which mainly includes four points:)

这个压缩包中包含了巴特沃斯滤波算法的高通和低通两款代码，同时还有几篇巴特沃斯算法的文章，记录了关于巴特沃斯算法的一些应用领域。(This compressed package contains the code of two of the high-pass and low-pass Butterworth filter algorithm, at the same time, there are few Butterworth algorithm articles recorded Butterworth algorithm applications.)

说明：  比SSIM更新的图像质量评判算法VSNR(image quality assessment VSNR and its matlab code)

通过区域迭代合并与定位切图的图像分割算法，非常好的。(Image Segmentation by Iterated Region Merging with Localized Graph Cuts)

数字图像处理(冈萨雷斯)书中的边界链码实现,该程序通过取网格,简化了编码的数据量(Digital image processing (Gonzalez) the boundary chain code book, realize that the procedures through access grid, simplifying the coding of data)

图像融合中常用的评价指标（非常全面）如：平均梯度、相关系数、信息熵、交叉熵、联合熵、均方误差、互信息、信噪比、峰值信噪比、均方根误差、空间频率、标准差、均值、扭曲程度、偏差指数等等。(Image fusion evaluation (very comprehensive): average gradient, correlation coefficient, entropy, cross entropy, joint entropy, mean square error and mutual information, signal to noise ratio, peak signal to noise ratio, root mean square error, spacefrequency, standard deviation, mean, distorting the degree of deviation index.)

Analysis of Singularities and Edge Detection using the Shearlet Transform

超分辨率图像处理方面的论文，很不错，可以看看(Super-resolution image processing papers, very good, you can take a look at)