Hough-Radon

格子波尔兹曼来实现图像修复，能够快速高效的实现图像修复(Lattice Boltzmann for image restoration, fast and efficient image restoration)

matlab语言，基于颜色特征提取的视频关键帧提取方法(matlab language, video key frame extraction method based on color feature extraction)

利用形态学的腐蚀运算提取图像边界 编写细化算法提取骨架的程序(Using morphology corrosion operation to extract image edgePreparation of thinning algorithm to extract the skeleton program)

图像加密 matlab程序 图像加密的程序 混沌系统图像加密的程序(encrypt photo)

能进行基于Arnold算法的正变换和反变换(Arnold-based algorithm to the transformation and anti-transform)

用于对原始图像根据指定图像的直方图进行匹配的matlab函数。用于改善图像的对比度。(Used for the original image histogram according to the specified image to match the matlab function. Be used to improve the image contrast.)

图像颜色一阶矩、二阶矩、三阶矩特征抽取。不带图像分块。(Image color first moment, second moment and three moments features)

暗原色先验的视频去雾算法 matlab实现(the code of defog using dark-chanel prior algorithm)

传统的图像分割方法可以分为基于阈值、边缘、区域的方法等，而传统的分割方法在复杂图像分割中效果不好。为了提高图像分割的性能，我们提出了一种基于局部自适应窗口的Otsu方法。首先，分析了传统阈值法在复杂图像分割中不能很好地形成的原因，比较了全局阈值和局部阈值对图像分割的影响。其次，提出了根据局部信息自适应地改变局部窗口大小的方法，分析了该方法的特点。最后，证明了所提出的新方法优于其他方法。实验结果表明，与其他传统方法相比，该方法能保持更多的细节，获得更满意的结果。(The traditional image segmentation methods can be divided into thresholding, edge and region methods. The traditional segmentation methods do not perform well in the segmentation of complicated images. In order to improve the performance of image segmentation, we propose an adaptive windowed range-constrained Otsu method based on local information.First, the reason why traditional thresholding methods do not perform well in the segmentation of complicated images is analyzed. Therein, the influences of global and local thresholdings on the image segmentation are compared. Secondly, a method of adaptively changing the local window size based on local information is proposed, and the characteristics of the proposed method are analyzed. Finally, the superiority of the proposed method over other methods, It is validated by the experiments that the proposed method can keep more details and acquire much more satisfying results as compared with the other conventional methods.)

Input .jpg; output .avi.