singalframenuc

说明：  整理了五种多曝光图像融合的代码，用于图像处理，图像去雾，有英文介绍(Five kinds of multi-exposure image fusion codes are sorted out.For image processing, image defogging, with English introduction)

imnoise的M文件，对数字图像的加噪声非常有用处！(imnoise M-files, plus noise on digital images is very useful!)

watermarking testing routine

实现BM3d denoising的源代码。 (BM3d denoising implementation source code)

非常有用的平滑l0算法代码程序，实现信号的稀疏重构(Very useful for smoothing the l0 algorithm code procedures to achieve signal sparse reconstruction)

寻找harris角点 利用ssd匹配角点(finding and matching harris points)

对图像进行特征提取，构造合适的特征集合，包括SFS,SBS,SFFS等(failed to translate)

对两幅图像提取特征点，并计算基本矩阵，对两幅图像进行极线矫正，并画出校正后图像特征点的极线，效果非常好(The two images of the image feature points in the feature points are extracted, and the calculated fundamental matrix, correction of lines of the two images, and after the draw correction lines, very good results)

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)

亚像素位移和变形梯度测量采用数字图像/散斑相关(Subpixel displacement and deformation gradient measurement using digital image/speckle correlation(„ DISC… ))