narrowband

gif图片转换成jpg文件格式 实例源码

这个是把图像归一化的程序，原始的图片可以随意设置，效果比较好。(This is the image of the normalized program, the original pictures can freely set, the effect is good. )

模式识别matlab工具箱，包含大约130个左右的模式识别类的程序。(Classification MatLab Toolbox)

设计了完整的基于颜色直方图的图像检索系统，效果还算可以(Design of a complete color histogram-based image retrieval systems, the effect can be fairly)

检测压力表的指针的角度信息，使用Hough变换的方法，去噪能力强。(Pressure gauge pointer is detected angle information, the use of Hough transform method, denoising ability.)

用分裂bregman方法解决图像模糊的matlab算法，很经典，大家可以参考一下(Split bregman method to solve the image blurring Matlab algorithm, it is classic, and we can refer to)

关于高光谱图像的图像处理技术，RX异常检测技术，代码功能强大，且代码注释很多，对学习很有帮助。(Hyperspectral image on image processing techniques, RX Anomaly Detection technology, powerful code feature, and many code comments, learning helpful.)

该算法首先通过字典学习得到含噪图像的冗余字典，然后对相似的图像块进行聚类构成块群，并通过迭代收缩和L1正则化约束，对同类的图像块在字典上进行稀疏表示，以达到降噪的目的。实验结果表明，在常规的图像处理上，本文提出的算法能较好的保留图像的结构信息，与K-SVD和BM3D等现有的流行算法相比，具有更高的峰值信噪比（PSNR）(It firstly get the redundant dictionary of a noised image by dictionary learning.Then,the image patches are gathered according to their similarities.Meanwhile,the similar patches get sparse representation showed in dictionaries by iterative shrinkage and L1 regularization constraints and eventually the image is restored and noise is removed.The experimental results indicate that the proposed algorithm can well preserve the structure information of the common image with a higher Peak Signal to Noise Ratio(PNSR),compared with state-of-the-art algorithms,such as K-SVD and BM3D)

基于LSB的音频水印嵌入提取算法，隐藏信息为一个文本文件(Extraction algorithm, the LSB-based audio watermark embedding hidden information into a text file)

应用傅立叶变换对图像处理，实现查看图像的功率谱，以及功率谱经过巴特沃斯高通、低通和高斯高通、低通滤波器后图像的功率谱，再把功率谱还原为图像的功能。(Fourier transform for image processing, see the image of the power spectrum, and the power spectrum after Butterworth high-pass, low-pass and high-pass Gaussian low-pass filter of the image after power spectrum, and then restore the power spectrum of the image.)