Image fusion

数字水印实现数字水印实现数字水印实现数字水印实现(The digital watermark digital watermark digital watermark to achieve)

这个是图像处理中的hough变换的C++源代码，工需要的同学参考学习。(This is the image processing hough transform C++ source code, engineering students need to learn.)

对于存在块效应图像进行质量评价 matlab程序 比较适合初学者(For the existence of blocking matlab image quality evaluation procedures are more suitable for beginners)

用surf和ransac实现两幅图像配准(Registration of two images with surf and RANSAC)

介绍2种计算分形维数的算法，里边包含2个程序，计算比较简单，分行维数主要是获取图像的表面粗糙度(fractal dimension)

图像检测的算法，Faster R-CNN算法，先对整张图像进行卷积计算，然后通过感兴趣区域池化层(RoI Pooling Layer)将选择性搜索算法推荐出来的候选区域和卷积网络计算出的特征映射图进行融合，得到候选区域对应的特征矢量，这种共享卷积计算的操作极大地减少了卷积计算的次数。而且这些特征矢量的维度统一，方便后续的分类工作。通过感兴趣区域池化层处理卷积特征，并将得到的特征送往两个并行计算任务进行训练，分类和定位回归。通过这些方法和改进的框架，Fast R-CNN 用更短的训练和测试时长，取得了比 R-CNN 更好的效果(Faster R-CNN algorithm first convolutes the whole image, then fuses the candidate regions recommended by the selective search algorithm and the feature mapping maps calculated by the convolution network through the RoI Pooling Layer to get the corresponding feature vectors of the candidate regions, which greatly reduces the number of convolution calculations. Moreover, the dimension of these feature vectors is unified, which facilitates the subsequent classification work. The convolution feature is processed by the pooling layer of the region of interest, and the obtained feature is sent to two parallel computing tasks for training, classification and positioning regression. Through these methods and improved framework, Fast R-CNN uses shorter training and testing time and achieves better results than R-CNN.)

读取gpr数据，并且对数据进行成像，没有进行任何数据处理(Read the GPR data and image the data without any data processing)

在图像分割中，FCM作为一种高效的分割方法收到广泛应用，FILCM分割算法是FCM算法的一种改进算法。(In image segmentation, FCM is widely used as an efficient segmentation method, and FILCM segmentation algorithm is an improved FCM algorithm.)

在做图像处理时用遇到图像的阈值分割，这是几种图像的阈值分割方法(In doing image processing with experience image threshold segmentation, which is one of several image thresholding segmentation method)

比较反滤波和维纳滤波对一幅图像复原的效果。从两方面出发，一是对运动模糊和均值模糊复原的能力，二是抗噪能力，各种情况分别都进行了对比(Comparison of inverse filtering and Wiener filtering on an image restoration results. Proceed in two ways, first on the motion blur and the mean fuzzy recovery capability and noise immunity, all cases are compared separately)