squint_sar

这是一个简单图像处理的GUI程序。实现的功能有：直方图统计，直方图拟合，边缘提取和自定义模板算子处理。界面采用的是ENVI 4.7的风格。(This is a simple image processing GUI program. Implemented features are: histogram, histogram fitting, edge extraction and custom template operator handling. Interface using the ENVI 4.7 style.)

超声图像处理软件dream，开源代码，用于仿真超声图像(Ultrasound image processing software dream, open-source code for the simulation of ultrasound images)

萤火虫算法 GSO算法用matlab编程实现(Firefly algorithm GSO algorithm using matlab programming)

基于Hough变换的圆检测 -matalab程序,能有效地检测圆曲线，在matalab下运行(Based on the Hough transform circle detection-matalab procedures, can effectively detect circular curve, in the run matalab)

说明：  Vibe 运动目标检测 基于matlab(Vbie Moving Target Detection)(Vibe moving target detection based on MATLAB (vbie moving target detection))

说明：  基于相邻像素间位异或的图像置乱算法,效果很好，改变图像直方图(Between the adjacent pixels based on the XOR bit of image scrambling algorithm, well, change the image histogram)

Zernike矩是一种具有尺度、移位和旋转不变性的正交不变矩，本设计的目的就是利用Zernike不变矩设计一种图像检索系统，该系统能够充分验证Zerinike矩的不变性及其在图像检索中的优良性能。具体内容包括： (1) 图像特征提取、统计特征提取； (2) Zernike不变矩及其应用方法； (3) 基于Zernike不变矩的图像检索系统。 (Zernike moments is a scale, shift and rotation invariant orthogonal invariant moments, the purpose of this design is the use of Zernike invariant moments design an image retrieval system, the system can fully verify Zerinike moments invariance and itsexcellent performance in image retrieval. Specific content includes: (1) image feature extraction, statistical feature extraction (2) Zernike invariant moments and its application method (3) based on Zernike Moment Invariant image retrieval system)

基于MATLAB GUI的图像处理界面，包括基本的图像处理功能(Graphical interface construction based on image processing)

一篇关于光声成像的图像重建算法研究，开发工具为matlab，罗列了很多理论知识(A photoacoustic imaging of image reconstruction algorithms, development tools matlab, listed a lot of theoretical knowledge )

图像分割是把图像划分成具有实际意义的互补交迭的区域的集合。在图像分割之前，图像区域的数目未知，而在分割后各个区域同时满足均匀性和连通性的条件，故图像分割是一个复杂的过程，目前大多数研究都是针对某一类型图像或者某一具体应用的分割。图像阈值化分割的基本思想是确定一个阈值，然后把每个像素点的像素值和阈值相比较，根据比较的结果把像素划分为两类，前景(1)或背景(0)。该方法的关键是确定一个最优的阈值。常用的阈值确定方法有直接门限法、类间最大方差法(otsu法)、分水岭算法、最小误差法、最大熵法等。该段代码就是图像的阈值分割。(The image segmentation is divided into the practical significance of the emerging area of complementary set. In the image segmentation, before the number of image region, the unknown, and after each area in division at the same time satisfy the uniformity and connectivity condition, so the image segmentation is a complicated process now, most research are for a certain type image or a particular application of the division. Image threshold segmentation of the basic idea is to determine a threshold, then put each pixel threshold value of the pixels and compared, according to the result of the comparison the pixel divided into two categories, the prospect (1) or background (0). This method is the key to determine an optimal threshold. Commonly used method to determine the threshold value of direct threshold method, the largest of the variance between method (otsu method), watershed algorithm, the minimum error method, the maximum entropy method, etc. When the code is image threshold segm)