拉普拉斯锐化（边缘检测）

用opencv实现对一幅图像的小波变换 opencv 图像二维小波变换(The wavelet transform of an image with opencv)

生成高斯光速及其变化束腰，平顶目标图片，和各种光束整形的图形(Generating gaussian beam waist and the changes the speed of light Ceiling target images, and various beam shaping graphics)

利用提升小波方法对小波去噪，效果要比一般的方法好。(Use of lifting wavelet method of wavelet denoising, the method more effective than the general good.)

说明：  将小波和各向异性扩散结合，对超声图像进行去噪。效果较好(The combination of wavelet and anisotropic diffusion for ultrasonic image denoising. Better)

说明：  直接可以运行的，基于matlab平台，里面包含了frost滤波,Lee滤波和kuan滤波，均可以直接运行。(Can run directly, based on MATLAB platform)

说明：  取一个最优阈值把原图像分为前景色与背景色，两部分的类间方差越大，说明两部分差别越大，便能有效的分割图像。(Image threshold segmentation)

说明：  实现图像模糊和恢复，有逆滤波恢复方法、傅里叶变换(Image blur and restoration are realized by inverse filtering and Fourier transform)

传统的几种图像增强与去噪方法，包括直方图处理，灰度变换，滤波去噪等几类。细分为直方图均衡化，直方图规定化，灰度线性变换，中值滤波，维纳滤波，同态滤波(The traditional methods of image enhancement and denoising include histogram processing, grayscale transformation, filtering and denoising. Subdivided into histogram equalization, histogram specification, gray level linear transformation, median filtering, Wiener filtering, homomorphic filtering)

说明：  采用考虑湍流内外尺度影响的退化模型来恢复大气湍流降质图像，运用包含了湍流内外尺度影响的波结构函数、折射率谱以及薄透镜成像的退化函数导出了新的退化模型。(A new degradation model includling the influence of outer-and inner-scale of turbulence is presented to restore atmospheric turbulence degradation images. )

局部二进制模式，LBP，是已被用于纹理特征的一个 分类。在本文中，提出了一种基于使用这些功能的方法 检测缺陷图案的面料。在培训阶段，在第一个步骤LBP算子是 施加到无缺陷织物样品，逐个象素和参考的所有的行（列） 特征矢量的计算。那么这个图像被分为Windows和LBP算子是 应用这些窗口的每一行（列）。根据与参考比较 特征向量一个合适的阈值，无缺陷的窗户被发现。在检测阶段中，一个 测试图像被划分成的窗户，并使用阈值时，有缺陷的窗口可以 检测到。该方法简单，灰度不变。由于其简单性， 线上实现是可能的。(Local Binary Patterns, LBP, is one of the features which has been used for texture classification. In this paper, a method based on using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row (column) of these windows. Based on comparison with the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is simple and gray scale invariant. Because of its simplicity, online implementation is possible as well. )