Repair

将处理过的二进制比特流写入图像，保存显示 (The treated binary bit stream to write the image )

本程序包括了三个程序，L1范数正则化，L2范数正则化，Tikhonov正则化超分辨率重建。经反复测试，没有BUG。(The program includes three procedures, L1 norm regularization, L2 norm regularization, Tikhonov regularization super-resolution reconstruction. After repeated testing, no BUG.)

说明：   最简单的差分格式有向前、向后和中心3种。 向前差分：f (n)=f(n+1)-f(n) 向后差分：f (n)=f(n)-f(n-1) 中心差分：f (n)=[f(n+1)-f(n-1)]/2(The easiest difference format forward, backward, and three kinds of centers. Forward differencing: f (n) = f (n+ 1)-f (n) Backward difference: f (n) = f (n)-f (n-1) Central difference: f (n) = [f (n+ 1)-f (n-1)]/2)

Multi-Scale Structural Similarity Index (MS-SSIM). Download here. Please cite the following paper in any published work if you use this software. Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," IEEE Asilomar Conference Signals, Systems and Computers, Nov. 2003.

说明：  使用LazySnapping算法对GrabCut算法进行改进(There are advanced segmentation algorithms in the literature which extends the concepts of graph cuts. Most prominent of them are Graph cuts and Lazy Snapping. Lazy napping is an interactive image cut out tool. Lazy Snapping separates coarse and fine scale processing, making object specification and detailed adjustment easy.)

8路串行lvds转单路并行axistream(8 single-pathLVDS to 1parallel AXI_Stream)

基于霍夫变换的直线检测，效果非常好，图片自己找，可能需要对图片格式做操作。(Line Detection Based on Hough Transform)

该源代码主要是利用bagging,adboosting等集成学习的方法进行图像融合处理，效果甚好！(The main source is the use of bagging, adboosting ensemble learning methods such as image fusion, the effect is very good!)

利用位图图像中的冗余数据位，将小文件嵌入到大的图像文件中，并保持原图像不变。(The use of bitmap images in the redundant data bit will be embedded in small files to large image files, and to maintain the original image unchanged.)

对ecg信号进行预处理，并检测ecg的r波，并进行特征提取，并在图中进行标注(On ecg signal preprocessing and detection of r ecg wave, and feature extraction, and marked on the drawing)