统计

已知Radon变换（CT原始数据）利用中心切片法则重建CT图像(Reconstruction using central slice theorem)

基于Tensorflow的Unet实现，里面有详细的教程。(TensorFlow for Unet, in which there are detailed teaching lecture.)

基于渐近区域的图像拼接matlab程序，可以实现全景图拼接(Based on the asymptotic region matlab image mosaic program, you can realize panorama stitching)

1、运动目标进行检测与跟踪，背景差分法，卡尔曼滤波，检测结果用红色外接矩形框表示，跟踪结果用绿色矩形框表示 2、采用平均背景法更新背景图像； 3、代码部分附详细解释(1. The moving target in the video sequence is detected and tracked, the moving target detection part uses the background difference method, the tracking part uses Calman filter, the detection result is represented by the red outer rectangle frame, and the tracking result is expressed in green rectangle frame. 2. The background image is updated with the average background method. 3. The part of the code is explained in detail)

说明：  这篇论文提出了一种较好的水下图像增强的方法。首先使用经过端到端训练的卷积神经网络去测量输入图片，同时以自适应双边滤波器对传输图片进行处理。接着提出一种基于白平衡的策略来消除图片的颜色偏差，用拉普拉斯金字塔融合获得无雾和色彩校正图像的融合结果。 最后，输出图像被转换为混合小波和方向滤波器组（HWD）域，用于去噪和边缘增强。 实验结果表明，该方法可以消除颜色失真，提高水下图像的清晰度。(This paper proposes a better underwater image enhancement method. Firstly, an end-to-end training convolutional neural network is used to measure the input image, and an adaptive bilateral filter is used to process the transmitted image. Then a strategy based on white balance is proposed to eliminate the color deviation of images. The fusion results of fog-free and color correction images are obtained by Laplacian pyramid fusion. Finally, the output image is converted into hybrid wavelet and directional filter bank (HWD) domain for denoising and edge enhancement. The experimental results show that the method can eliminate color distortion and improve the clarity of underwater images.)

fisher 的lda算法，注释非常详细清晰(fisher)

说明：  Projection Metric Learning on Grassmann Manifold with Application to Video based Face Recognition

dct和混沌相结合的图像加密，速度较慢但可以用来学习(DCT and chaotic combination of image encryption, slower but can be used to study)

图像盲解卷积复原，一模方法，算法在压缩包内(Blind Image Deconvolution recovery, a model approach, the algorithm in the compressed package)

基于多阈值的图像分割()