fastNLmeans2

insar图像数据，envisat卫星单视复数数据，可在matlab中直接打开(insar data, envisat single look complex data, which can be opened directly in matlab)

图像处理中的深度学习，主要是原理的讲解，比较易懂,对于。(Image processing in deep learning, mainly to explain the principle, relatively easy to understand.)

图像DCT变换水印提取 图像DCT变换水印提取(DCT image watermark images extracted from DCT watermark image watermark extraction DCT)

基于粒子群算法的（pso）的otsu图像分割算法。(Based on Particle Swarm (pso) image segmentation algorithm of Otsu.)

VS 环境下利用opencv 实现图像的傅里叶变换，输入图片，保存输出结果。(Under VS environment use opencv to achieve image Fourier transform, the input image and save the output.)

读取RAW格式文件，将其转换为BMP格式文件并存储，主要实现灰度图像的转换(Read the RAW format, convert it to BMP format file and stored, mainly to achieve the conversion of grayscale images)

用matlab实现的表面质量检测的程序，主要检测图像缺陷，并标示出缺陷位置，工业生产过程中亦都有用。(Using matlab to achieve the surface quality inspection procedures, the main defect detected image, and mark the location of the defect, the industrial production process also has a use.)

一套基于VC++的遥感影象处理系统,可以实现空间域,频率域以及光谱的处理(yi tao ji yu VC++ de yaohan yingxiang chuli xitong)

首先生成如下形式的图片：图中的白条为7象素宽，210象素高，白条间的宽度为17象素。 应用多种滤波器进行处理：算术均值，几何均值，谐波均值，逆谐波均值，中值滤波，中点滤波，最大值、最小值滤波等。注意，需改变模板大小。详细比较上述的结果，并分析原因。 (First generation following form images: figure IOUs 7 pixels wide, 210 pixels high, the width is 17 pixels between IOUs. Using a variety of filters for processing: the arithmetic mean, geometric mean, harmonic mean, inverse harmonic mean, median filtering, filtering midpoint, maximum, minimum filtering. Note that the need to change the template size. A detailed comparison of these results and analyze the reasons.)

说明：  1.学习使用fft2,ifft2,fftshift,ifftshift函数。用fft2函数对”lena.bmp”做二维傅立叶变换，并显示其频谱，用fftshift函数将频谱坐标原点移动到图像中心。用ifft2函数做傅立叶反变换。（提示：显示频谱时，可以先对频谱取对数，这样可以压缩动态范围很大的频谱，获得更好的显示效果）(1. Learn to use fft2, ifft2, fftshift, ifftshift functions. With the FFT 2 function, the lena.bmp'The two-dimensional Fourier transform is performed and the spectrum is displayed. The origin of the spectrum coordinate is moved to the center of the image by using the FFT shift function. The inverse Fourier transform is done with ifft2 function. (Note: when displaying the spectrum, the logarithm of the spectrum can be taken first, so that the spectrum with a large dynamic range can be compressed to obtain better display effect.))