预处理

说明：  水下图像处理方法之一的图像融合算法matlab实现(Matlab implementation of image fusion algorithm, one of the classical image processing methods)

说明：  关于数字图像一些典型隐写的隐写分析。隐写、分析和提取的讲解(Steganalysis of Some Typical Steganography in Digital Images)

一种基于SIFT的图像配准方法，目前使用最广泛的一种配准方法。性能优。(One kind SIFT image registration method based on the most widely used kind of registration method. Excellent performance.)

针对同一场景可见光与红外图像的融合问题,提出了一种新的多尺度对比度塔图像融合方法. 该方法 利用对比度金字塔数据结构得到图像的多分辨序列,采用基于视觉特性的融合算子在图像的相应各级上融 合源图像的细节,再通过金字塔逆变换重构出最终融合图像. 这种图像处理方法具自适应性,不随各自输入 图像的灰度特性而改变,同时增强了融合图像的对比度,产生了较好的视觉处理效果.(for the same scene of visible and infrared images of integration, proposed a new multi-scale tower contrast images. The use of contrast pyramid data structure Image multiresolution sequence, based on visual characteristics of the integration operator of the image corresponding levels of integration details of the source image, through the inverse pyramid transform eventually fused images. This image processing method has adaptability without their input image with the gray characteristics change, while enhancing the integration of the contrast of the image. have a better visual effect.)

IDL开发的求取两幅图像相关系数的程序，用于影像融合质量评价(IDL to strike a developed procedures for the correlation coefficient of two images for image fusion quality evaluation)

说明：  基于IHS融合算法的多光谱与全色图像融合代码，使用于融合ETM+遥感图像(IHS-based multi-spectral and panchromatic image fusion code)

使用frequency-tuned saliency算法计算图像的saliency值，基于此saliency值将图像中的显著区域提取出来。(Using frequency-tuned saliency saliency value of image algorithm, based on the saliency value of the significant image area extracted.)

通过多尺度分解，实现医学图像的图像融合，达到更好的效果(Through multi-scale decomposition, medical image fusion can be achieved to achieve better results.)

细菌觅食算法与粒子群优化算法相结合的程序，可以实现适应度函数的最值计算，有很好的参考价值。(Bacterial Foraging algorithm and particle swarm optimization combined program, you can achieve the best fitness function value, and have a good reference value.)

OMP算法的matlab的详细描述，对学习正交匹配追踪算法和稀疏分解是很有用的(the OMP algorithm is describled by matlab,and it is very useful for us to study OMP algorithm and sparse decomposition.)