Gamma-correction

说明：  用于MATLAB的支持向量机的工具箱，用于训练与验证(Support vector machine (SVM) for MATLAB toolbox, used for training and validation)

Graph Cuts是一个图像分割方法，具有交互式分割，分割效果很好。(Graph Cuts is an image segmentation method, with interactive segmentation, segmentation works well.)

Efficient Computation of Shortest Paths in Networks Using Particle Swarm Optimization and Noising Metaheuristics

说明：  这个压缩包里只要给出了边缘保持系数和等效视数的计算方法(This compression is given as long as the edge of the bag to maintain the equivalent depending on the number of coefficients and the calculation of)

求取一幅二值图像包含目标区域的最小矩形框，返回矩形框的上下左右边界，然后根据边界坐标在图上画出目标区域。(Strike the rectangle that contains the smallest rectangular target area in a binary image, return to the top and bottom rectangular box around the border, and then draw the boundary on the map coordinates of the target area.)

说明：  对限制对比度自适应直方图均衡化算法adapthisteq进行了解释。可用于图像增强(Contrast to the limited adaptive histogram equalization algorithm adapthisteq is explained. Can be used for image enhancement)

说明：  此程序为地震层析成像的LSQR算法，此算法用来解反问题。(This procedure for seismic tomography LSQR algorithm, the algorithm used to solve inverse problems.)

说明：  语义分割，训练样本，模型训练，成果展示，深度卷积神经网络(Semantic segmentation, training samples, model training, achievement display, deep convolution neural network)

图像分割是图像分析的关键步骤，而水平集方法是图像分割的一项热门方法，它有着许多算法不具有的优点，尤其可用于分割背景复杂信息难以提取的医学图像。这里是一些水平集分割图像的matlab的实现例子，希望可以帮到大家。(Image segmentation is the key step of image analysis, while the level set method is a popular image segmentation method, it has many advantages that the algorithm does not have, especially can be used for medical image segmentation of complex background information is difficult to extract. Here is some level set segmentation image matlab example, I hope we can help you.)

针对CT 医学图像和MR I 医学图像成像特点, 提出了基于快速整数提升小波变换的融合方法。在CT 和 MR I 两幅医学图像配准的前提下, 利用提升小波变换把图像分解成低频和高频子图像, 对于小波变换后的高频 子图像, 选择区域标准差大的作为融合后的子图像 对于低频子图像, 采用加权融合, 最后进行小波逆变换, 得到 融合后的图像, 并对融合后图像用信息熵、平均梯度、相关系数的指标进行评价。实验结果表明, 基于快速整数提 升小波变换融合中, 小波高低频系数采用不同的规则能够取得更好的融合效果, 其轮廓清晰。该算法能够提升融 合后的医学图像信息量, 同时有效地保护图像的细节信息, 在执行时间和图像质量上均优于普通小波算法。(A imed at the characterist ics of CT andMR Imedical images, a new image fu sion al2 go rithm based on the fast in t lif t ing w avelet t ran sfo rm s is p ropo sed. Two o riginal images are decompo sed by the fast in t lif t ing w avelet t ran sfo rm s. The fu sion ru le based on the max imum standard deviat ion value variance is u sed to fu se the h igh f requency sub2image, w h ile a w eigh t2 ed average fu sed ru le is app lied by coeff icien t s of the low f requency. F inally, the fu sion image is recon st ructed fo r perfo rm ing inverse fast in t lif t ing w avelet t ran sfo rm s. The fu sion image is evaluated by en t ropy, average gradien t, and co rrelat ion coeff icien t s. Experimen tal resu lt show s that the fu sion image hasmo re info rmat ion than o riginal images and imp roves the quali2 ty of o riginal images. M eanw h ile, the fu sion image p ro tect s detail characterist ics of the image, thu s the real2t ime p rocess and image qualit ies are w ell than tha)