SAR影像单视数据转为多视数据，数据间相互转化，快捷处理SAR数据...

基于OpenCV的计算机视觉技术实现 作者:陈胜勇 刘盛 等 此书第二章代码1-OpenCV based on computer vision technology: Chensheng Yong Sheng-code such as this book Chapter 1

能比较全面的图像处理系统。主要内容包括：位图基础、图像的显示、图像的几何变换、图像灰度变换、图像的平滑处理、图像锐化处理及边缘检测、图像分割及测量、图像的形态学处理、图像的变换域处理及应用、图像的合成、24位彩色图像的处理、JPEG图像的压缩编码-can more comprehensive image processing system. Main contents include : bitmap based, image display, image geometry transformation, gray transform, image smoothing, image sharpening and edge detection, image segmentation and measurement, image morphology, Image Transform and application processing, image synthesis, 24-bit color image processing, JPEG image compression coding

一个对图像进行二值化处理的代码,有灰度拉伸的比较-an image managing algorithm, set an threshold for the manage to turn it to an two-value manage.

随机产生符号数和概率算法的实现 很使用 特别对图象处理-randomly generated number and the probability of achieving algorithm is to use a special pair of image processing

平滑处理是一种衫的数字图像处理技术,主要目的是为了减少图像的噪声.可用滤波的方法实现.-Smoothing is a kind of shirt deal with the digital image processing technology, the main purpose is to reduce the image noise. Filtering method can be used.

英文介绍关于pgm图像格式，可以提高英文水平，并获得关于图像方面的知识-English introduction on pgm image format, you can improve the standard of English and knowledge on the image

TIDSP DM642上用的图像锐化程序-TIDSP DM642 use the image sharpening process

Visual C++ 数字图像处理典型算法及实现(4)-Visual C++ digital image processing algorithms and implementation of a typical (4)

Visual C++MATLAB图像处理与识别实用案例精选之对比度拉伸的例子-Visual C MATLAB image processing and recognition Featured"s practical case examples contrast tensile

实现简单图像处理，包括256色转灰度图、Hough变换、Walsh变换、中值滤波、二值化变换、亮度增减、傅立叶变换、反色、取对数、取指数、图像平移、图像旋转、图像细化、图像缩放、图像镜像、均值滤波、对比度拉伸、拉普拉斯锐化（边缘检测）、方块编码、梯度锐化、灰度均衡、直方图均衡、离散余弦变换、维纳滤波处理、逆滤波处理、阈值变换、高斯平滑。-simple image processing, including color to grayscale 256, the Hough transform, Walsh transform, Median filtering, binary conversion, brightness changes, Fourier transform, and color-from the few to admit index, image shifting, image rotation, refined images, image scaling, image mirroring, mean filtering, contrast stretching, Laplacian sharpening (edge detection), block codes, Sharpening gradient, gray balance, histogram equalization, discrete cosine transform, Wiener filter, reverse filtering, threshold transform, Gaussian smoothing.