HMarkDemo

医学图像分割是数字图像分析中的重要环节，是长期以来国内外研究的热点问题，本代码专注于血管图像领域的分割。(Medical image segmentation is an important part of digital image analysis, is a hot topic at home and abroad for a long time, this code is to focus on areas of blood vessel image segmentation method.)

基于NSCT与HSI的图像融合算法，采用非下采样的Contourlet变换和HSI彩色图像融合。(image fusion algorithms based on NSCT and HSI)

python写的图像相似度算法。比较图像相似度，选出最相思的图片(python write the image similarity algorithm. Compare image similarity, image to select the most Acacia)

说明：  图像处理所涉及的256转灰度，二值化等等，每个部分都是独立可执行的工程文件，很方面的代码复用(256 involved in image processing turn gray, binary, etc., are independent of each part of the project executable file, it' s code reuse)

图像融合评价程序，通过均值，标准偏差，熵，梯度，相关系数，光谱扭曲度等多个指标对融合图像进行评价比较。(image fusion evaluation procedures, the mean, standard deviation, entropy, gradient, and the correlation coefficient. spectral distortion degree of integration of various indicators to evaluate the comparative images.)

1．选择一幅图像，对该图像进行DCT块操作（将图像分成8×8小块，对每个小块进行DCT变换）。 2.利用屏蔽矩阵对DCT系数进行区域选择，然后进行逆变换（IDCT），合成原图像，计算合成图像的PSNR。 3.选择一小波函数，对一幅图像进行分解，显示各变换系数，观察其规律，并进行小波逆变换，合成原图像。 (1. Select an image, the image DCT block operations (the image is divided into 8 × 8 small pieces of each small piece for DCT transform). 2. The use of shielding on the DCT coefficient matrix of regional choices, and then inverse transform (IDCT), synthesis of the original image, calculating the synthetic image PSNR. 3. To select a wavelet function, decomposition of an image to show the transform coefficients, to observe its laws, and inverse wavelet transform, the original synthetic image.)

对图像灰度二值化，提取图像中某个色彩的部分并分割(The image is binarized and the part of a color in the image is extracted and segmented)

编程实现了抑制sar图像相干斑噪声的精致lee滤波算法，效果很好(refined lee)

实现了读取一个bmp图片的傅里叶变换 使用C语言实现 读取一个 24位 图 转换为8位灰度图后，进行傅里叶变换处理（二维） 采用快速傅里叶变换——蝶形算法 北航图像处理作业~需要进一步交流 请联系qq 422563357(Reads a bmp image to achieve a Fourier transform using C language Northern image processing operations)

大连理工大学卢湖川教授团队显著性检测方法4(Visual Tracking via Probability Continuous Outlier Model)