数字图像处理_第三版_英_答案

本程序，直接用MATLAB实现otsuf算法，并使用此算法对经典图像进行分割。(This procedure, directly with MATLAB to achieve otsuf algorithm, and use this algorithm to segment the classic image.)

1AttributeQuery属性查询2BufferAnalysis缓冲区分析3ComExtender使用Com组件扩展GIS服务器4Common_CustomDataSource_CSharp自定义数据源，XML，遥感影像数据5ContextManager通过Context为服务器添加图层，使用Image代替MapControl6ContextMenu右键菜单(全图，输出地图)7CustomRender自定义专题图实现(GDI)，非AO8CustomTask自定义任务，ITask，IFloatingPanelTask9CustomTool点、线、面、巨型、多边形、圆查询10DynamicAddResource动态添加本地/远程数据源，通过实例化MapResourceItem11ExportLegend输出图例，使用Image显示12FromControl使用用拖拉控件实现简单的地图浏览13FullSearch查询，全文查询14MapServerBrowserCS端使用AGS地图服务显示地图15NorthAmericaSample模版实例程序16Security移除图层、工具栏项、Task项17SetLayerVisibility控制图层可视性，使用Div代替Toc18SimpleAjaxAJAX原理实例19SpatialQueryCOMCom组件扩展服务器，空间查询20ThemeMap专题图，使用细粒度AO实现21VegSOE服务器对象扩展22webctrl_clientHtc，Web行为文件的使用23WebServiceTestGISWeb服务的使用与创建

这是文章Nonlocally Centralized Sparse Representation for SR的代码(this is the code of Nonlocally Centralized Sparse Representation for SR)

若干经典的无参考图像质量评价算法matlab版本，附带详细的注释。(Several classic non reference image quality evaluation algorithms matlab version, with detailed notes.)

bm4d图像去噪算法，可有效地去除图片中的噪点。(Bm4d image denoising algorithm, can effectively remove the noise in the picture.)

这是我的大作业，作用是测试车流量并计数，使用高斯模型对车辆进行定位分割，已验证成功内附测试视频(This is my big job, the role is to test the traffic flow and counting, the use of the Gauss model of the vehicle positioning and segmentation, has been verified successfully included test video)

图像修复是对图像中破损区域进行信息填充，以减少图像破损所带来的信息损失的过程。 传统的图像修复方法需要依赖图像的具体结构来制定相应的修复方法，压缩感知理论的提出，使得可以利 用信号的稀疏性来对图像进行修复。基于K 奇异值分解（KSVD）与形态学成分分析（MCA，Morphological Component Analysis）的图像修复方法首先采用形态学成分分析方法对破损图像进行特征分析，将其分解 为结构部分和纹理部分；然后基于学习型字典KSVD分别对这两部分进行过完备字典训练；最后利用训练得 到的字典实现对破损图像的修复。相比于传统的图像修复方法，该方法具有适应性强、修复效果好等优点(Image inpainting is to fill the missing data in corrupted images and thus to reduce the information loss of damaged image. Traditional inpainting algorithms are dependent on specific structure of target images compressive sensing theory makes is possible to realized image inpainting with signal sparsity. This paper proposes a novel inpainting algorithm based on KSVD and MCA algorithm, which first decomposes the image into texture part and structure part, and then trains the two dictionaries for these two parts with KSVD and reconstructs the original image with these two trained dictionaries. Experiment indicates that the proposed algorithm is of better adaptability and performance as compared with traditional algorithms.)

采用基于信道非均匀性校正改进的RLS算法实现对探测器的非均匀性校正(An improved RLS algorithm based on channel nonuniformity correction is used to correct the nonuniformity of detector.)

Solutions to problems in the field of digital image processing generally require extensive experimental work involving software simulation and testing with large sets of sample images.

两张图片合成实例，可类举