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数字图像处理实验-图像处理-含实验题目及源代码(Pseudo-Color Image Processing (a) Implement Fig. 6.23, with the characteristic that you can specify two ranges of gray-level values for the input image and your program will output an RGB image whose pixels have a specified color corresponding to one range of gray levels in the input image, and the remaining pixels in the RGB image have the same shade of gray as they had in the input image. You can limit the input colors to all the colors in Fig. 6.4(a). (b) Download the image in Fig. 1.10(4) and process it with your program so that the river appears yellow and the rest of the pixels are the same shades of gray as in the input image. It is acceptable to have isolated specs in the image that also appear yellow, but these should be kept as few as possible by proper choice of the two gray-level bands that you input into your program.)

针对遥感图像可控参数误差造成的图像模糊，进行模糊核估计(Remote sensing images for image blur caused by controllable parameter error, blur kernel estimation)

将需要处理的图像进行九等分，输出分割的图像并确认是否保存。——By LPETERPAN(Will need to deal with the image of nine equal, the output image segmentation and confirm whether to save.—— LPETERPAN)

Images of diffraction

目标运动的真实轨迹和估计轨迹，以及所进行的误差分析(Target motion trajectory and estimate the true trajectory, and error analysis conducted)

基于相似度的图像质量评价算法Structural SIMilarity (SSIM) index的实现。(This is an implementation of the algorithm for calculating the Structural SIMilarity (SSIM) index between two images. Please refer to the following paper: Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity" IEEE Transactios on Image Processing, vol. 13, no. 4, pp.600-612, )

使用极限学习机ELM算法进行遥感图像分类的源代码，很好的例子(Use extreme learning machine ELM remote sensing image classification algorithm source code, a good example)

说明：  八值图像连通区域标记，为每个连通区域分配一个唯一的标号，处理后的图像按照从左到右，从上到下的顺序获得连续的标号(Binary image connected region eight marks, for each connected region a unique label distribution deal with in accordance with the image from left to right, from top to bottom order of access to continuous labeling)

图象经过gabor小波滤波。gabor小波与图像卷积。(Image after wavelet filtering gabor. gabor wavelet and image deconvolution.)

互信息和梯度相结合的图像配准算法，在配准中引入了图像的空间信息(Mutual information and gradient combination of image registration algorithm, the introduction of registration in the image of the spatial information)