Estimation-master

对二维码图像进行灰度化、中值滤波、二值化、边缘检测、霍夫变换等预处理(The two-dimensional code image is grayscale, median filter, two valued, edge detection, Hough transform and other pre-processing.)

求灰度共生矩阵及其各要素用matlab实现(For gray-scale co-occurrence matrix and its elements realize with matlab)

DNF按钮生成器易语言源码程序,例程结合易语言扩展界面支持库和图像格式转换支持库，实现生成DNF按钮。(DNF Button Builder easy language source program , a routine extension interface combined with easy language support library and image format conversion support library that implements generate DNF button.)

说明：  LSB算法在图像中隐藏文本信息，实现效果良好，分享给大家(The LSB algorithm hides the text information in the image to achieve good results and share it with everyone)

此程序为shearlet程序，用于对图像进行分解变换，有兴趣的可以下载(This procedure for shearlet program for image decomposition transform, are interested can download)

RMSHE算法（recursive mean-separate histogram equalization）。基本原理是将图像根据平均灰度进行分割，各部分分别进行直方图均衡。文件中包含RMSHE的matlab源代码，论文，以及输入图像(RMSHE algorithm (recursive mean-separate histogram equalization). The basic principle is that the image based on the average gray-scale is divided, each part respectively histogram equalization. File contains RMSHE matlab source code, papers, as well as the input image)

说明：  分块直方图均衡化处理图像 用户输入分块的x,y方向的分块个数，即可将图形分成各个小块进行直方图均衡化。 大津法处理图形，包括一维大津法和二维大津法的实现 (the realization of the Histogram equalization algorithm. the realization of otsu algorithm to process the images)

Input .jpg; output .avi.

一个基于Gabor滤波器组的多特征融合的图像检索算法（CBIR based on Multi-Features Combining Multi-parameters from Gabor Filter-bank, 简称multi法），融合了基于Gabor 小波系数的标准方差，峰态，能量，平滑度和一致性度量五个特征量度，实验表明，该算法是一种更加有效的图像检索方法。(we propose a CBIR algorithm based on Multi-Features Combining Multi-parameters from Gabor Filter-bank(called multi algorithm for short), which use Gabor filter banks and combine five features including standard deviation, kurtosis, energy, smoothness and uniformity together. The results of experiments show that this algorithm is a more effective method used for image retrieval.)

图像修复是对图像中破损区域进行信息填充，以减少图像破损所带来的信息损失的过程。 传统的图像修复方法需要依赖图像的具体结构来制定相应的修复方法，压缩感知理论的提出，使得可以利 用信号的稀疏性来对图像进行修复。基于K 奇异值分解（KSVD）与形态学成分分析（MCA，Morphological Component Analysis）的图像修复方法首先采用形态学成分分析方法对破损图像进行特征分析，将其分解 为结构部分和纹理部分；然后基于学习型字典KSVD分别对这两部分进行过完备字典训练；最后利用训练得 到的字典实现对破损图像的修复。相比于传统的图像修复方法，该方法具有适应性强、修复效果好等优点(Image inpainting is to fill the missing data in corrupted images and thus to reduce the information loss of damaged image. Traditional inpainting algorithms are dependent on specific structure of target images compressive sensing theory makes is possible to realized image inpainting with signal sparsity. This paper proposes a novel inpainting algorithm based on KSVD and MCA algorithm, which first decomposes the image into texture part and structure part, and then trains the two dictionaries for these two parts with KSVD and reconstructs the original image with these two trained dictionaries. Experiment indicates that the proposed algorithm is of better adaptability and performance as compared with traditional algorithms.)