code

说明：  当前论文主要考虑的是非信号依赖的高斯噪声下的图像恢复，本程序实现了泊松噪声下的图像恢复，泊松噪声为信号依赖噪声，能够更加有效逼近实际成像系统噪声。( This is the code that was used in the papers "A Nonnnegatively Constrained Convex Programming Method for Image Reconstruction", "Total Variation-Penalized Poisson Likelihood Estimation for Ill-Posed Problems", "Tikhonov Regularized Poisson Likelihood Estimation: Theoretical Justification and a Computational Method", "An Efficient Computational Method for Total Variation with Poisson Negative-Log Likelihood", "An Analysis of Regularization by Diffusion for Ill-Posed Poisson Likelihood Estimation," "An Iterative Method for Edge-Preserving MAP Estimation when Data-Noise is Poisson", and finally, "Regularization Parameter Selection Methods for Ill-Posed Poisson Maximum Likelihood Estimation". See my publications page for more details. The main algorithm is for nonnegatively constrained, regularized Poisson likelihood estimation. At this point you can choose Tikhonov, total variation regularization, and diffusion regularization. A number of other methods are also implemented. Regularizatio)

watermarking testing routine

一个基于形态学图像细化的程序，处理效果挺不错的(Morphological image thinning based on the procedures)

说明：  关于GLSL的点光源，载入模型，纹理。。。。。。。。。。(light engine code OpenGL.)

说明：  NDVI最大值合成法批量计算Python代码(Python Code for Batch Computing by NDVI Maximum Composition Method)

说明：  基于MP的信号稀疏分解参考程序。源程序是《信号与图像的稀疏分解及初级应用》中的(MP sparse decomposition based on the signal reference program. Source is &quot Signal and image sparse decomposition and primary application&quot in the)

说明：  平台基于visual studio2015，使用opencv函数实现中值滤波，进行图像处理(median blur based on opencv)

MATLAB可见水印、不可见水印添加程序源代码，可见水印部分采用中值法，不可见部分采用DWT变换。(MATLAB visible watermark, invisible watermark to add the source code, we can see the watermark part in value method, the invisible part on DWT.)

目标检测，利用上下文关系 (object detection using context)

说明：  性能上比经典分水岭有很大的提高。特别对于自然图像的分割，效果特别好。(Performance than the classical watershed have greatly improved. Especially for natural image segmentation, the effect particularly good.)