很好的一本关于傅立叶变换的课本，适合通讯，电子，物理电子等相关专业学生，当然了，是英文版的，需要硬着头皮看了。

很好的图像处理参考书, 涵盖面广而且很全!

桥接文本和表格数据以进行跨域文本到SQL的语义解析 这是以下论文的官方代码版本： Xi Victoria Lin，Richard Socher和Caiming Xiong。 。 EMNLP 2020的发现。 概述 跨域表格语义解析（X-TSP）是在给某个数据库发出自然语言问题的情况下预测可执行结构化查询语言的任务。 在训练期间，该模型可能会或可能不会看到目标数据库。 该库实现 一个强大的基于序列到序列的跨域文本到SQL语义解析器，在两个广泛使用的基准数据集： 和上实现了最先进的性能。 从改编而来的一组用于解析，标记化和验证SQL查询的。 通过修改正式的语言预处理和后处理模块，可以使解析器适合于学习从文本到其他结构化查询语言（例如的映射。 模型 我们的模型将自然语言话语和数据库（模式+字段选择列表）作为输入，并生成SQL查询作为标记序列。 我们应用模式指导的解码和后处理，以确保最终

快速优化的图像/视频增强方法 它是由Java实现的一组图像/视频增强方法，用于解决一些常见任务，例如除雾，去噪，水下去除，低照度增强，特性，平滑等。 请注意，此存储库是多个图像/视频处理存储库的集成，这些独立的存储库将在以后弃用。 RemoveBackScatter-已删除，其zip文件在此处可用： 。 OptimizedContrastEnhance-已删除，其zip文件位于此处： 。 将不推荐使用，其zip文件位于此处： HazeRemovalByDarkChannelPrior-已删除，其zip文件在此处可用： ALTMRetinex-已删除，其zip文件在此处可用：

心音-深度学习 该项目旨在在低功耗ARM处理器（例如在树莓派上找到的处理器）上运行。 目的是将该软件打包到一个小型硬件设备中，发展中国家的护理工作者可以使用该设备来检测心脏病的早期发作。

With continuing progress in VLSI and ASIC technologies, digital signal processing (DSP) algorithms have continued to find great use in increasingly wide application areas. DSP has gained popularity also in embedded systems although these systems set challenging constraints for implementations. Embedded systems contain limited resources, thus embedded DSP systems must balance tradeoffs between the requirements on computational power and computational resources. Energy efficiency has been important in batterypowered devices, but nowadays also the limited heat dissipation in small devices calls for low-power consumption. Successful implementation of DSP applications in embedded systems requires tailoring, which in turn sets challenges for design methodologies.

Probability and Random Processes with Application to Signal Processing，经典的信号处理教程，英文原版，非扫描版，带部分书签

Preface xi Acknowledgements xvii 1 Image Processing 1 1.1 Basic Definitions 2 1.2 Image Formation 3 1.3 Image Processing Operations 7 1.4 Example Application 9 1.5 Real-Time Image Processing 11 1.6 Embedded Image Processing 12 1.7 Serial Processing 12 1.8 Parallelism 14 1.9 Hardware Image Processing Systems 18 2 Field Programmable Gate Arrays 21 2.1 Programmable Logic 21 2.1.1 FPGAs vs. ASICs 24 2.2 FPGAs and Image Processing 25 2.3 Inside an FPGA 26 2.3.1 Logic 27 2.3.2 Interconnect 28 2.3.3 Input and Output 29 2.3.4 Clocking 30 2.3.5 Configuration 31 2.3.6 Power Consumption 32 2.4 FPGA Families and Features 33 2.4.1 Xilinx 33 2.4.2 Altera 38 2.4.3 Lattice Semiconductor 44 2.4.4 Achronix 46 2.4.5 SiliconBlue 47 2.4.6 Tabula 47 2.4.7 Actel 48 2.4.8 Atmel 49 2.4.9 QuickLogic 50 2.4.10 MathStar 50 2.4.11 Cypress 51 2.5 Choosing an FPGA or Development Board 51 3 Languages 53 3.1 Hardware Description Languages 56 3.2 Software-Based Languages 61 3.2.1 Structural Approaches 63 3.2.2 Augmented Languages 64 3.2.3 Native Compilation Techniques 69 3.3 Visual Languages 72 3.3.1 Behavioural 73 3.3.2 Dataflow 73 3.3.3 Hybrid 74 3.4 Summary 77 4 Design Process 79 4.1 Problem Specification 79 4.2 Algorithm Development 81 4.2.1 Algorithm Development Process 82 4.2.2 Algorithm Structure 83 4.2.3 FPGA Development Issues 86 4.3 Architecture Selection 86 4.3.1 System Level Architecture 87 4.3.2 Computational Architecture 89 4.3.3 Partitioning between Hardware and Software 93 4.4 System Implementation 96 4.4.1 Mapping to FPGA Resources 97 4.4.2 Algorithm Mapping Issues 100 4.4.3 Design Flow 101 4.5 Designing for Tuning and Debugging 102 4.5.1 Algorithm Tuning 102 4.5.2 System Debugging 104 5 Mapping Techniques 107 5.1 Timing Constraints 107 5.1.1 Low Level Pipelining 107 5.1.2 Process Synchronisation 110 5.1.3 Multiple Clock Domains 111 5.2 Memory Bandwidth Constraints 113 5.2.1 Memory Architectures 113 5.2.2 Caching 116 5.2.3 Row Buffering 117 5.2.4 Other Memory Structures 118 vi Contents 5.3 Resource Constraints 122 5.3.1 Resource Multiplexing 122 5.3.2 Resource Controllers 125 5.3.3 Reconfigurability 130 5.4 Computational Techniques 132 5.4.1 Number Systems 132 5.4.2 Lookup Tables 138 5.4.3 CORDIC 142 5.4.4 Approximations 150 5.4.5 Other Techniques 152 5.5 Summary 154 6 Point Operations 155 6.1 Point Operations on a Single Image 155 6.1.1 Contrast and Brightness Adjustment 155 6.1.2 Global Thresholding and Contouring 159 6.1.3 Lookup Table Implementation 162 6.2 Point Operations on Multiple Images 163 6.2.1 Image Averaging 164 6.2.2 Image Subtraction 166 6.2.3 Image Comparison 170 6.2.4 Intensity Scaling 171 6.2.5 Masking 173 6.3 Colour Image Processing 175 6.3.1 False Colouring 175 6.3.2 Colour Space Conversion 176 6.3.3 Colour Thresholding 192 6.3.4 Colour Correction 193 6.3.5 Colour Enhancement 197 6.4 Summary 197 7 Histogram Operations 199 7.1 Greyscale Histogram 199 7.1.1 Data Gathering 201 7.1.2 Histogram Equalisation 206 7.1.3 Automatic Exposure 210 7.1.4 Threshold Selection 211 7.1.5 Histogram Similarity 219 7.2 Multidimensional Histograms 219 7.2.1 Triangular Arrays 220 7.2.2 Multidimensional Statistics 222 7.2.3 Colour Segmentation 226 7.2.4 Colour Indexing 229 7.2.5 Texture Analysis 231 Contents vii 8 Local Filters 233 8.1 Caching 233 8.2 Linear Filters 239 8.2.1 Noise Smoothing 239 8.2.2 Edge Detection 241 8.2.3 Edge Enhancement 243 8.2.4 Linear Filter Techniques 243 8.3 Nonlinear Filters 248 8.3.1 Edge Orientation 250 8.3.2 Non-maximal Suppression 251 8.3.3 Zero-Crossing Detection 252 8.4 Rank Filters 252 8.4.1 Rank Filter Sorting Networks 255 8.4.2 Adaptive Histogram Equalisation 260 8.5 Colour Filters 261 8.6 Morphological Filters 264 8.6.1 Binary Morphology 264 8.6.2 Greyscale Morphology 269 8.6.3 Colour Morphology 270 8.7 Adaptive Thresholding 271 8.7.1 Error Diffusion 271 8.8 Summary 273 9 Geometric Transformations 275 9.1 Forward Mapping 276 9.1.1 Separable Mapping 277 9.2 Reverse Mapping 282 9.3 Interpolation 285 9.3.1 Bilinear Interpolation 286 9.3.2 Bicubic Interpolation 288 9.3.3 Splines 290 9.3.4 Interpolating Compressed Data 292 9.4 Mapping Optimisations 292 9.5 Image Registration 294 9.5.1 Feature-Based Methods 295 9.5.2 Area-Based Methods 299 9.5.3 Applications 305 10 Linear Transforms 309 10.1 Fourier Transform 310 10.1.1 Fast Fourier Transform 311 10.1.2 Filtering 318 10.1.3 Inverse Filtering 320 10.1.4 Interpolation 321 10.1.5 Registration 322 viii Contents 10.1.6 Feature Extraction 323 10.1.7 Goertzel’s Algorithm 324 10.2 Discrete Cosine Transform 325 10.3 Wavelet Transform 328 10.3.1 Filter Implementations 330 10.3.2 Applications of the Wavelet Transform 335 10.4 Image and Video Coding 336 11 Blob Detection and Labelling 343 11.1 Bounding Box 343 11.2 Run-Length Coding 346 11.3 Chain Coding 347 11.3.1 Sequential Implementation 347 11.3.2 Single Pass Algorithms 348 11.3.3 Feature Extraction 350 11.4 Connected Component Labelling 352 11.4.1 Random Access Algorithms 353 11.4.2 Multiple-Pass Algorithms 353 11.4.3 Two-Pass Algorithms 354 11.4.4 Single-Pass Algorithms 356 11.4.5 Multiple Input Labels 358 11.4.6 Further Optimisations 358 11.5 Distance Transform 359 11.5.1 Morphological Approaches 360 11.5.2 Chamfer Distance 360 11.5.3 Separable Transform 362 11.5.4 Applications 365 11.5.5 Geodesic Distance Transform 365 11.6 Watershed Transform 366 11.6.1 Flow Algorithms 366 11.6.2 Immersion Algorithms 367 11.6.3 Applications 369 11.7 Hough Transform 370 11.7.1 Line Hough Transform 371 11.7.2 Circle Hough Transform 373 11.7.3 Generalised Hough Transform 374 11.8 Summary 375 12 Interfacing 377 12.1 Camera Input 378 12.1.1 Camera Interface Standards 378 12.1.2 Deinterlacing 383 12.1.3 Global and Rolling Shutter Correction 384 12.1.4 Bayer Pattern Processing 384 Contents ix 12.2 Display Output 387 12.2.1 Display Driver 387 12.2.2 Display Content 390 12.3 Serial Communication 393 12.3.1 PS2 Interface 393 12.3.2 I2C 395 12.3.3 SPI 397 12.3.4 RS-232 397 12.3.5 USB 398 12.3.6 Ethernet 398 12.3.7 PCI Express 399 12.4 Memory 400 12.4.1 Static RAM 400 12.4.2 Dynamic RAM 401 12.4.3 Flash Memory 402 12.5 Summary 402 13 Testing, Tuning and Debugging 405 13.1 Design 405 13.1.1 Random Noise Sources 406 13.2 Implementation 409 13.2.1 Common Implementation Bugs 410 13.3 Tuning 412 13.4 Timing Closure 412 14 Example Applications 415 14.1 Coloured Region Tracking 415 14.2 Lens Distortion Correction 418 14.2.1 Characterising the Distortion 419 14.2.2 Correcting the Distortion 421 14.3 Foveal Sensor 424 14.3.1 Foveal Mapping 425 14.3.2 Using the Sensor 429 14.4 Range Imaging 429 14.4.1 Extending the Unambiguous Range 431 14.5 Real-Time Produce Grading 433 14.5.1 Software Algorithm 434 14.5.2 Hardware Implementation 436 14.6 Summary 439 References 441 Index 475 x Content

Efficient gamut clipping for color image processing using LHS and YIQ 很经典的一篇图像增强处理的方法介绍，在LHS和YIQ空间都可以，调整饱和度，亮度等，还有超出RGB范围时，快速做gamut clipping，在显示和拍照里都用的到

概述 Triangler是使用生成低多边形图像的工具。 目录 样本 执照 安装 下载Windows Binary 您可以下载Windows二进制文件。 请注意，Windows二进制文件比从源代码运行的速度慢。 下载页面 注意：二进制发行版通常较旧，需要重建。 有关用法，请参见#usage部分。 从源头运行 您需要Python 3.6或更高版本。 我强烈建议使用Anaconda虚拟环境。 您可以在此处下载Anaconda 。 请按照下面的手册使用Anaconda为Triangler创建python虚拟环境。 $ conda create -n triangler python=3.8 $ activate triangler (triangler)$ git clone https://github.com/tdh8316/triangler.git (triangler)