ssim

可以将连续图片转换成视频文件，值得参考学习(Convert a continuous image into a video file)

Retinex算法对较暗区域的图像处 理有明显效果,改进的Retinex方法则是基于经典Retinex,对具有较高亮度的遥感图像通过图像变 换方法达到去除模糊的目的。 (Retinex algorithm for image processing on the dark region was effective to improve the Retinex method is based on the classic Retinex, on remote sensing images with high brightness achieved by an image transformation method aims to remove ambiguity.)

图像融合的各种Q值评价指标的代码，包括Q0,Qe,Qw,Qabf,vif等，非常适合图像融合的质量评价(Q values ​ ​ of various uation code image fusion, including Q0, Qe, Qw, Qabf, vif, etc., is very suitable for quality uation of image fusion)

图像预处理的过程彩色图像去噪，彩色图像增强，彩色图像锐化(Image pre-process color image denoising, color image enhancement, color image sharpening)

在matlab中读入视频序列，并显示出来。对视频流文件进行按帧读取，并计算帧与帧之间差值，并统计差值的分布。(Read into the video sequence in matlab, and displayed. Streaming video, read the press frame, and calculate the difference between frames, and statistical difference between the distribution.)

我的毕业设计，用matlab编的关键帧提取的代码，参考了光流法的代码。是基于帧差的欧式距离，均值，方差，差异系数下的关键帧提取。代码调试通过，运行结果理想，与大家分享下。(I graduated from design, matlab key frame extraction for the code, with reference to code optical flow method. Frame difference is based on the Euclidean distance, the mean, variance, difference coefficient under the key frame extraction. Debugging through the code, run the results are satisfactory, the next to share with you.)

锐化效果显示图像 例子

说明：  应用DCT字典和KSVD学习字典，对图像进行简单的修复，可行性较高，速度不是很快。(Simple image restoration is feasible and not very fast.)

基于MATLAB对输入图像的进行灰度化处理(Grayscale processing of the input image based on MATLAB)

针对CT 医学图像和MR I 医学图像成像特点, 提出了基于快速整数提升小波变换的融合方法。在CT 和 MR I 两幅医学图像配准的前提下, 利用提升小波变换把图像分解成低频和高频子图像, 对于小波变换后的高频 子图像, 选择区域标准差大的作为融合后的子图像 对于低频子图像, 采用加权融合, 最后进行小波逆变换, 得到 融合后的图像, 并对融合后图像用信息熵、平均梯度、相关系数的指标进行评价。实验结果表明, 基于快速整数提 升小波变换融合中, 小波高低频系数采用不同的规则能够取得更好的融合效果, 其轮廓清晰。该算法能够提升融 合后的医学图像信息量, 同时有效地保护图像的细节信息, 在执行时间和图像质量上均优于普通小波算法。(A imed at the characterist ics of CT andMR Imedical images, a new image fu sion al2 go rithm based on the fast in t lif t ing w avelet t ran sfo rm s is p ropo sed. Two o riginal images are decompo sed by the fast in t lif t ing w avelet t ran sfo rm s. The fu sion ru le based on the max imum standard deviat ion value variance is u sed to fu se the h igh f requency sub2image, w h ile a w eigh t2 ed average fu sed ru le is app lied by coeff icien t s of the low f requency. F inally, the fu sion image is recon st ructed fo r perfo rm ing inverse fast in t lif t ing w avelet t ran sfo rm s. The fu sion image is evaluated by en t ropy, average gradien t, and co rrelat ion coeff icien t s. Experimen tal resu lt show s that the fu sion image hasmo re info rmat ion than o riginal images and imp roves the quali2 ty of o riginal images. M eanw h ile, the fu sion image p ro tect s detail characterist ics of the image, thu s the real2t ime p rocess and image qualit ies are w ell than tha)