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对变形的条纹图像进行傅立叶变换得到二维频谱图并提取基频(Fourier transform in deformation stripes image two-dimensional spectrum and extract the fundamental frequency )

ISAR点目标RD成像算法,距离向包络对齐，多普勒中心相位补偿(Algorithm for ISAR point target imaging)

该程序为傅立叶变换，用来将时域信号进行频域信号分析，保护功率谱、能量谱等。(The program for the Fourier transform, time-domain signal to the frequency domain signal analysis, to protect the power spectrum, the energy spectrum.)

说明：  缺陷检测，定位缺陷位置。主要用于工业之中螺帽的螺纹线的检测(Defect detection, location of defect location. Detection of screw thread for nuts in industry)

EDLines是一种快速直线检测算子，2012年在ICCV上提出，也是目前处理直线检测最快的算法之一，该算法包含三个步骤：（1）边缘提取：利用Edge Drawing (ED)算法[28,29]从灰度图像中提取边缘片段；（2）线段检测：利用最小二乘法提取直线段；（3）线段确认：遵循Helmholtz定律，从已提取的直线段中摒弃虚假线段。EDLines算法的优越性得益于Edge Drawing (ED)算法能够从灰度图像中准确、快速、稳定地提取出光滑、完整的边缘片段。Edge Drawing (ED)算法包含以下步骤： （1）采用size=5*5,σ=1的高斯核对灰度图像进行平滑滤波，去除噪声； （2）采用一种常用的梯度算子，如Prewitt、Sobel或Scharr等计算平滑后图像中每个像素点的梯度幅度和梯度方向； （3）将梯度图中邻域内幅度最大的像素点标记为锚点，这些锚点成为图像边缘点(edge elements)的概率很大； （4）将相邻的锚点连接成边缘线。从一个锚点起始，ED利用相邻像素的梯度幅度和方向在梯度为最大值的锚点之间游走。 (Edge Drawing (ED) is our recently-proposed, novel, fast edge detection algorithm. What makes ED stand out the existing edge detectors, e.g., Canny, is the following: While the other edge detectors give out a binary edge image as output, where the etected edge pixels are usually independent, discontinuous entities ED produces a set of edge segments, which are clean, contiguous, i.e., connected, chains of edge pixels. Thus, while the output of the other edge detectors requires urther processing to generate potential object boundaries, which may not even be possible or result in inaccuracies ED not only produces perfectly connected object boundaries by default, but it also achieves this in blazing speed compared to other edge detector.)

说明：  直方图调整并显示对比，直方图调整后都需要对比一下调整前与调整后的区别，并显示。此程序适合初学者学习。(Histogram and adjust the display contrast)

strtok(char s[], const char *delim) 用来将字符串分割成一个个片段。参数s指向欲分割的字符串，参数delim则为分割字符串中包含的所有字符。当strtok()在参数s的字符串中发现参数delim中包涵的分割字符时,则会将该字符改为

说明：  This is BM3D algorithm implemented according to the paper: K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080-2095, August 2007.(This is BM3D algorithm implemented according to the paper: Image denoising by sparse 3D transform-domain collaborative filtering)

低对比度图像小目标识别,top-hat.海平面小目标(荧光轨迹,舰船)识别(top-hat, dim small target detecting)

给定的n个点在二维平面上,发现的最大数量点躺在同一直线。(Given n points on a 2D plane, find the maximum number of points that lie on the same straight line. )