SSIM_Matlab_

DISKREET: FOR CHANGING DISKREET LEVEL OF AN IMAGE

P0301：数字图像矩阵数据的显示及其傅立叶变换 P0302：二维离散余弦变换的图像压缩 P0303：采用灰度变换的方法增强图像的对比度 P0304：直方图均匀化 P0305：模拟图像受高斯白噪声和椒盐噪声的影响 P0306：采用二维中值滤波函数medfilt2对受椒盐噪声干扰的图像滤波 P0307：采用MATLAB中的函数filter2对受噪声干扰的图像进行均值滤波 P0308：图像的自适应魏纳滤波 P0309：运用5种不同的梯度增强法进行图像锐化 P0310：图像的高通滤波和掩模处理 P0311：利用巴特沃斯（Butterworth）低通滤波器对受噪声干扰的图像进行平滑处理 P0312：利用巴特沃斯（Butterworth）高通滤波器对图像进行锐化处理 (P0301: digital image data matrix display and Fourier Transform P0302: two-dimensional discrete cosine transform image compression P0303: using gray transform method to enhance the contrast of the image P0304: histogram equalization P0305: Simulation images by white Gaussian noise and salt and pepper noise P0306: 2D median filtering function medfilt2 right by Salt and Pepper image noise filtering P0307: using MATLAB functions filter2 of noise images mean filtering P0308: Image Adaptive Filter Weiner P0309: use five different gradient Enhancement Act for image sharpening P0310: Images of the high-pass filtering and mask handling P0311: use Butterworth (Butterworth) low-pass filter on the noise in the image smoothing P0312: use Butterworth ( Butterworth) high-pass filter for image sharpening)

说明：  matlab区域填充的具体算法及演示，绝对原创(the algorithm of fill an origin used matlab)

visual c++ 工具 c语言 实现的灰度共生矩阵。(visual c++ tool c language GLCM.)

说明：  短时傅里叶变换的一些简单的演示，其中有矩形窗和高斯窗(some demos for the short time fourier transform)

最为经典的ROA和ROEWA算子，用于SAR图像边缘检测，并附有一定的实例图像，以说明实验结果。(ROA and ROEWA algorithms are given in order to detect the edge of images, and some images are attached illustrating the experimental results.)

实现菲涅耳数字全息的方里面包括同轴和离轴 欢迎大家下载(feinieer shuziquanxi··)

相机标定源代码，包含张正友，Tsai等基于matlab的相机标定方法。(Camera calibration source code, including Zhang Zhengyou, Tsai camera calibration method based on Matlab.)

说明：  对图像进行处理，直接在RGB图片上画直线，矩形，多边形，写字。(Image processing, including the pictures directly on the RGB lines, rectangles, polygons, write.)

说明：  最小噪声分离变换（Minimum Noise Fraction Rotation，MNF Rotation）工具用于判定图像数据内在的维数（即波段数），分离数据中的噪声，减少随后处理中的计算需求量(Minimum Noise Fraction Rotation (MNF Rotation) tool is used to determine the intrinsic dimension (i.e. band number) of image data, separate the noise in data, and reduce the computational requirements in subsequent processing.)