斑点检测

说明：  使用说明： 全网唯一的一款能够快速完全浏览+整理微信图片视频的软件! 智能的发现微信聊天中的显示过的图片、视频，并将它们汇总在一起，方便您随时浏览。 您可以将您亲人的照片、工作中的重要图片、搞笑的视频等资源，整理收藏起来。 提供图片、视频的备份功能，收藏就是备份到手机的存储卡上。(Instructions: The only software on the whole network that can browse and organize the pictures and videos of Wechat quickly and completely! Intelligent discovery of pictures and videos displayed in the micro-chat, and aggregate them together to facilitate your browsing at any time. You can organize and collect photos of your relatives, important pictures at work, funny videos and other resources. Provide the backup function of pictures and videos, and the collection is backed up to the memory card of the mobile phone.)

能够实现土壤水分和光照度的数据的显示与保存(Data can be achieved degree of soil moisture and light display and preservation)

SVDD classifier 即支持向量数据描述分类器，能将数据分为两类。本文件也提供了训练数据和测试数据，方便进行测试。(Support vector data description classifier. Data can be divided into two categories .This document also provides training data and test data to make it easy to test the program.)

说明：  fast RPCA背景检测,较为经典的一种算法，亲测可用(fast rpca method for background selection)

可以计算图像结构相似性，评价图像质量(Similarity of image structure can be calculated and image quality can be evaluated)

简单的Halcon 找圆算法，首先进行圆检测(识别圆、圆拟合)，再找出坐标位置(A simple Halcon algorithm for finding circles firstly conducts circle detection (identifying circle and circle fitting), and then finds the coordinate position)

< 数字图像处理算法及应用>这本书的相关代码(Image Processing Algorithm and Practices is an important book in IP and AI. High-pass Filter is very normal and practical tool.)

如何获取屏幕上各颜色的红、绿、蓝值(how to access the colors on the screen in red, green and blue values)

Bezier curve creation and plotting with fortran, greek .docs

本程序是基于单幅图像的超分辨率算法，通过对输入的高分辨率图像进行下采样得 % 到原始低分辨率图像，对输入的低分辨率图像插值后，利用DWT将低分辨率图像分 % 解为不同的4个子带；同时直接对对分辨率图像进行SWT处理。由SWT得到的高频子 % 带来修正DWT得到的高频子带。最后，通过逆离散小波变换（IDWT）组合修正的高 % 频子带和低分辨率图像，得到输出的超分辨率图像。通过计算PSNR和SSIM评价结果。(This program is a super-resolution algorithm based on a single image, which is sampled from the high resolution image of the input. % to the original low resolution image, after interpolating the input low resolution image, the low resolution image is divided by DWT. The% solution is different 4 subbands, and the resolution image is processed by SWT. High frequency subsets obtained by SWT % brings the high frequency subband obtained by the modified DWT. Finally, the height of the combination of inverse discrete wavelet transform (IDWT) % frequency subband and low resolution image, and the output super-resolution image is obtained. The results were evaluated by calculating PSNR and SSIM.)