“数字图像处理”笔记 -- 几个实验 (DIP)

Posted on September 27, 2020 (珠海)
图像处理 

基本几何变换

手写数字识别

• https://www.jb51.net/article/133880.htm

• https://github.com/HanpuLiang/Simple-Handwritten-Numerel-Recogntion

scikit-image 进行图像分割

• https://blog.csdn.net/weixin_42142987/article/details/87717861

邮政编码识别

• 图像识别与处理笔记 —— 字符识别、邮政编码识别（10 月 8 日暂存）

• 2010 毕业设计

• 2005.3 毕业设计 / 2005.3 毕业设计

• OpenCV：裁剪 6 位数的手写邮政编码

汽车牌照识别

二值化阈值集中常用的选取方法：双峰（2-Mode）法，P 参数法，Otsu 法，迭代法。

大津法（OTSU 法或最大类间方差法）

假定一个阈值，方差越大，说明差别越大。

迭代法（最佳阈值法）

一些代码

• from https://github.com/wzh191920/License-Plate-Recognition

• 代码还可以 车牌检测与定位

• 代码还可以 PYTHON 实现车牌识别

• 使用 opencv 和 python 实现车牌区域提取

• opencv + python 车牌识别

• https://github.com/DataXujing/vehicle-license-plate-recognition

案例分析

toP61-1 手写数字识别

toP58-2 邮政编码识别

toP60-3 汽车牌照识别

toP62-4 条形码识别

toP63-5 印刷体汉字识别

toP54-6 人脸识别

toP57-7 细胞识别与统计

toP55 SAR 图像分割 / 遥感图像配准

谱聚类（求解规范化 Laplace 矩阵的特征值和特征向量）

toP56/59 总变差（TV）模型·图像恢复

图象恢复是一个客观过程，需要利用某些先验知识从退化和干扰的图像中去除模糊因素和噪声、尽可能恢复图像的本来面貌。复原技术一般是先把退化和干扰模型化，然后采用相反的过程进行处理，复原图像。

数字图像处理 源码

• http://accu.cc/content/pil/agwn/

• 学习笔记 https://github.com/LiuFG/digital_image_process

• 孔洞填充、骨架算法、边界提取。冈萨雷斯《数字图像处理》Python（第三版） https://github.com/qcymkxyc/Image-Process

• https://github.com/iwuqing/Digital-Image-Processing

• 冈萨雷斯《数字图像处理》读书笔记 https://github.com/MAhaitao999/digital_image_processing

• 数字图像处理（第三版） https://github.com/ljy9826/Digital-Image-Processing_code

CPP 源码

冈萨雷斯《数字图像处理》RafaelC.Gonzalez

一堆复习笔记

• 数字图像处理学习笔记

• 数字图像处理笔记

• 这个不错 数字图像处理 —— 知识点

• 数字图像处理 总结 https://blog.csdn.net/u011178262/article/details/79067643

• 数字图像处理复习总结 https://blog.csdn.net/sunshine_lyn/article/details/85400015

• 冈萨雷斯《数字图像处理》读书笔记 https://blog.csdn.net/weixin_37625243/article/details/102556940

一堆 Python 代码

• 实现了离散傅里叶变换：https://github.com/tesfagabir/Digital-Image-Processing/blob/master/01-Implementing-Discrete-Fourier-Transform-Using-Python.ipynb

• 实现了快速傅里叶变换：https://github.com/tesfagabir/Digital-Image-Processing/blob/master/02-Implementing-Fast-Fourier-Transform-Using-Python.ipynb

• 几个简单的处理：https://github.com/mohammaduzair9/Basic-Image-Processing

• 一个偏门的库 Library for Digital Pathology Image Processing https://github.com/histolab/histolab

• 毛发去除算法，有点意思：https://github.com/sunnyshah2894/DigitalHairRemoval

• 4-simple-steps-in-Builiding-OCR https://github.com/Nagakiran1/4-simple-steps-in-Builiding-OCR

一堆滤镜

• 一批复古风：A set of image filters crafted and curated by This Is My Jam https://github.com/thisismyjam/jam-image-filter

• 一个库，风格丰富：Image processors and filters for use with ImageKit https://github.com/fish2000/instakit

• Simple python demos of Guided Image Filtering (Python). https://github.com/tody411/GuidedFilter

• 专门为除去照片中腿毛等体毛而设计。Specially designed for image leg/arm hair removal. https://github.com/FerryYoungFan/FanselineImageToolbox

• BeautyCamera https://github.com/PerpetualSmile/BeautyCamera

  饱和度调整，明度调节，皮肤识别，皮肤磨皮（value1 精细度，value2 程度），美白算法（皮肤识别），美白算法（人脸识别），Gamma 矫正，怀旧滤镜，木刻滤镜，细节增强，风格化，铅笔画（彩色）。

  https://github.com/Ssssix40/Cameo1.1/blob/master/filters.py

• An Wiener Filter Implementation for Image Processing Task https://github.com/tranleanh/wiener-filter-image-restoration

• image filtering techniques in python with examples https://github.com/vikasgola/image-filtering

• Commonly used image filters. 🌎 包罗常见的图像滤波器。https://github.com/JNingWei/Image_Filter

Python ImageFilter, ImageEnhance

• 纯 Python 综合图像处理小工具（2）图像增强 https://www.cnblogs.com/sopic/p/4537718.html

• 纯 Python 综合图像处理小工具（3）10 种滤镜算法 https://www.cnblogs.com/sopic/p/4538052.html

  BLUR、CONTOUR、DETAIL、EDGE_ENHANCE、EDGE_ENHANCE_MORE、EMBOSS、FIND_EDGES、SMOOTH、SMOOTH_MORE、SHARPEN（GaussianBlur、UnsharpMask、Kernel、RankFilter、MedianFilter、MinFilter、MaxFilter、ModeFilter）

  各种效果：https://zhuanlan.zhihu.com/p/62737338

  ImageFilter.BLUR	模糊滤镜
  ImageFilter.CONTOUR	轮廓
  ImageFilter.DETAIL	细节滤镜
  ImageFilter.EDGE_ENHANCE	边界加强
  ImageFilter.EDGE_ENHANCE_MORE	边界加强（阀值更大）
  ImageFilter.EMBOSS	浮雕滤镜
  ImageFilter.FIND_EDGES	边界滤镜
  ImageFilter.SMOOTH	平滑滤镜
  ImageFilter.SMOOTH_MORE	平滑滤镜（阀值更大）
  ImageFilter.SHARPEN	锐化滤镜
  GaussianBlur（radius）	高斯模糊
  UnsharpMask	USM 锐化
  MedianFilter	中值滤波

• 纯 Python 综合图像处理小工具（4）自定义像素级处理（剪纸滤镜）https://www.cnblogs.com/sopic/p/4665946.html

参考

• [1] bilibili
• [2] 标题自动编号
• [3] 课程目录
• [4] GITHUB 数学公式展开：https://chrome.google.com/webstore/detail/mathjax-plugin-for-github/ioemnmodlmafdkllaclgeombjnmnbima

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Reference snapshot, script generated automatically.

• [1] https://www.jb51.net/article/133880.htm
• [2] https://github.com/HanpuLiang/Simple-Handwritten-Numerel-Recogntion
• [3] https://blog.csdn.net/weixin_42142987/article/details/87717861
• [4] https://blog.csdn.net/mao_hui_fei/article/details/78177471
• [5] http://www.doc88.com/p-693272517244.html
• [6] https://wenku.baidu.com/view/c26f1ea5492fb4daa58da0116c175f0e7cd119ea.html
• [7] https://max.book118.com/html/2018/1108/8013115051001132.shtm
• [8] https://stackoom.com/question/3xNO6/Opencv-%E8%A3%81%E5%89%AA-%E4%BD%8D%E6%95%B0%E7%9A%84%E6%89%8B%E5%86%99%E9%82%AE%E6%94%BF%E7%BC%96%E7%A0%81
• [9] https://github.com/wzh191920/License-Plate-Recognition
• [10] https://www.freesion.com/article/5566190803/
• [11] https://www.freesion.com/article/9575144324/
• [12] https://www.jianshu.com/p/08ddeb0d8878
• [13] https://www.cnblogs.com/kekexxr/p/11574589.html
• [14] https://github.com/DataXujing/vehicle-license-plate-recognition
• [15] http://accu.cc/content/pil/agwn/
• [16] https://github.com/LiuFG/digital_image_process
• [17] https://github.com/qcymkxyc/Image-Process
• [18] https://github.com/iwuqing/Digital-Image-Processing
• [19] https://github.com/MAhaitao999/digital_image_processing
• [20] https://github.com/ljy9826/Digital-Image-Processing_code
• [21] http://zuzhiang.cn/2019/09/24/DIP/
• [22] https://a-suozhang.xyz/2019/09/06/Diigital-Image-Procss/
• [23] https://www.jianshu.com/p/926d6c15dbf9
• [24] https://blog.csdn.net/u011178262/article/details/79067643
• [25] https://blog.csdn.net/sunshine_lyn/article/details/85400015
• [26] https://blog.csdn.net/weixin_37625243/article/details/102556940
• [27] https://github.com/tesfagabir/Digital-Image-Processing/blob/master/01-Implementing-Discrete-Fourier-Transform-Using-Python.ipynb
• [28] https://github.com/tesfagabir/Digital-Image-Processing/blob/master/02-Implementing-Fast-Fourier-Transform-Using-Python.ipynb
• [29] https://github.com/mohammaduzair9/Basic-Image-Processing
• [30] https://github.com/histolab/histolab
• [31] https://github.com/sunnyshah2894/DigitalHairRemoval
• [32] https://github.com/Nagakiran1/4-simple-steps-in-Builiding-OCR
• [33] https://github.com/thisismyjam/jam-image-filter
• [34] https://github.com/fish2000/instakit
• [35] https://github.com/tody411/GuidedFilter
• [36] https://github.com/FerryYoungFan/FanselineImageToolbox
• [37] https://github.com/PerpetualSmile/BeautyCamera
• [38] https://github.com/Ssssix40/Cameo1.1/blob/master/filters.py
• [39] https://github.com/tranleanh/wiener-filter-image-restoration
• [40] https://github.com/vikasgola/image-filtering
• [41] https://github.com/JNingWei/Image_Filter
• [42] https://www.cnblogs.com/sopic/p/4537718.html
• [43] https://www.cnblogs.com/sopic/p/4538052.html
• [44] https://zhuanlan.zhihu.com/p/62737338
• [45] https://www.cnblogs.com/sopic/p/4665946.html
• [46] https://www.bilibili.com/video/BV1tx41147Tx
• [47] https://www.cnblogs.com/36bian/p/7609304.html
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• [49] https://chrome.google.com/webstore/detail/mathjax-plugin-for-github/ioemnmodlmafdkllaclgeombjnmnbima

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