fft

说明：  边缘检测是图像处理和计算机视觉中的基本问题，边缘检测的目的是标识数字图像中亮度变化明显的点。图像属性中的显著变化通常反映了属性的重要事件和变化。这些包括（i）深度上的不连续、（ii）表面方向不连续、（iii）物质属性变化和（iv）场景照明变化。边缘检测是图像处理和计算机视觉中，尤其是特征提取中的一个研究领域。 一阶:?Roberts Cross算子，Prewitt算子，Sobel算子， Kirsch算子，罗盘算子； 二阶： Marr-Hildreth，在梯度方向的二阶导数过零点，Canny算子，Laplacian算子。(Edge detection is a basic problem in image processing and computer vision. Significant changes in image attributes usually reflect significant events and changes in attributes. These include (I) discontinuities in depth, (ii) discontinuities in surface orientation, (iii) changes in material properties, and (iv) changes in scene lighting. Edge detection is a research field in image processing and computer vision, especially in feature extraction.)

基于小波阈值法来实现图像去噪，这是小波阈值去噪的程序代码。(Based on wavelet thresholding denoising method to achieve this is wavelet thresholding procedure code.)

计算图像调制传递函数（MTF）。提高图像清晰度。可以处理普通图像以及卫星照片(Calculation of image modulation transfer function (MTF). Improve image clarity. Can deal with ordinary images and satellite photos)

主分量分析的源代码，附有简单的解释说明，主要对图像矩阵做降维处理，适用多幅图像的变化检测(Principal component analysis of the source code, a simple explanation, the main image matrix reducing dimension, suitable for image change detection)

利用遥感做变化检测的时候可以用到的数据。(Data Used in Change Detection Using Remote Sensing)

图片的预处理 将图片读入之后 经过灰度处理 中值滤波（多次） sobel算子运算 二值化运算 最后做细化 处理 显示并保存 细化后的图象(Pretreatment of the picture after the picture to read through the gray scale median filter processing (multiple) sobel operator Binary operator to do the final thinning operation display and save the image after thinning)

说明：  采用surf算法对两幅较低重叠度的图像进行拼接(Image mosaic using surf algorithm)

精心收集的几个去噪算法，包括基于视觉的非线性去噪、BLS_GSM算法、NL-Bayes图像去噪、KSVD去噪等，内容为核心代码，需要配置完善。 (Denoising algorithm of several carefully collected, including visual based nonlinear denoising, BLS_GSM algorithm, NL-Bayes image denoising, KSVD denoising, the content as the core code, the need to improve the allocation of. )

说明：  matlab实现的简单的车牌识别，附有相关模板库(matlab chepaishibie,and xiangguanmubanku)

基于二维直方图的最大模糊熵阈值图像分割，相比于一维最大模糊熵，分割效果更好。隶属度函数采用S函数(Based on two-dimensional histogram maximum fuzzy entropy threshold image segmentation, compared to one-dimensional maximum fuzzy entropy segmentation better)