localostu

检测鼠标信息，C++Builder精选编程学习源码，很好的参考资料。(Detecting mouse information, C++Builder select learning programming source code, a good reference.)

智能算法实现图像分割，读取一张黑白图片进行阈值分割。(Intelligent algorithms for image segmentation, read a black and white image thresholding.)

本工程在C6678上实现了BMP图像的读取，滤波，锐化等操作。可以作为多核DSP图像处理的资料，自己在CCS6平台上测试验证没有问题(This project has realized the reading, filtering and sharpening of the BMP image on C6678. It can be used as a multi-core DSP image processing data, and to test validation on the CCS6 platform)

该程序主要实现图像的无损压缩，具体算法包括： （1）整数小波变换＋SPIHT无损压缩 其中整数小波可以采用JPEG2000提供的5－3小波，也可以采用S＋P变换，本程序均已给出。 （2）DPCM＋算术编码 DPCM采用的是JPEG中采用的方法； （3）JPEG－LS标准 JPEG－LS的无损压缩性能非常好，超过JPEG2000。(The program mainly realize lossless image compression, the specific algorithm include: (1) integer wavelet transform SPIHT Integer Wavelet lossless compression which can be used to provide the 5-3 wavelet JPEG2000, can also adopt the S P transform, the procedures are given. (2) DPCM arithmetic coding DPCM is used in JPEG methodology (3) JPEG-LS standard JPEG-LS lossless compression of very good performance, more than JPEG2000.)

说明：  用于实现图像超分辨率重建的不同算法的matlab程序，可直接编译(Used to achieve super-resolution image reconstruction algorithm matlab program can be compiled directly)

说明：  图像融合算法，exercise1:为基于小波变化与局部能量的图像融合方法 exercise2:为基于自适应算法的图像融合算法 myfunction1~myfunction5:为性能指标评价函数(Image fusion algorithm, exercise1: wavelet-based change and the local energy method of image fusion exercise2: adaptive algorithm based on image fusion algorithm myfunction1 ~ myfunction5: performance indicators for the evaluation function)

自适应正则化超分辨率重建 是： M. G. Kang这人写的IEEE上发表的论文 可以参考下算法( General Choiceof the Regularization Functional in Regularized Image Restoration)

Viola-Jones Fast Face Detection的matlab实现。(Viola-Jones Fast Face Detection Mex Implementation)

L0梯度最小化算法，2011年由xuli提出的算法(L0 gradient minimization algorithm, in 2011 xuli put forward the algorithm )

直方图是以图形化参数来显示图片曝光精确度的手段，其描述的是图片显示范围内影像的灰度分布曲线。它可以帮助分析图片的曝光水平等一些信息。(Histogram is a graphical display picture exposure parameters to measure the accuracy of its description is the picture display grayscale images within the distribution curve. It can help you analyze the picture and some information on exposure levels.)