wcompress_upload.m

对各种不同的传感器测得数据进行融合处理，从而获得精确的环境信息，能够做出更正确的判断。这个是一位非常优秀的老师的课件，非常值得一看！是关于数据融合方面的(On a variety of sensor data fusion measured to obtain accurate environmental information to make more accurate judgments. This is a very good teacher of the courseware, is worth a visit! Data fusion)

基于暗通道先验理论的去雾算法，使用rgb暗通道计算大气光及透射率获得去雾图像(Since the scene radiance is usually not as bright as the atmospheric light, the image after haze removal looks dim. So we increase the exposure of J(x) for display.)

指纹增强，使用的是ＳＴＦＴ短时傅里叶方法，可用。(Fingerprint enhancement, using the STFT short time Fourier method, is available.)

本文针对多光谱图像配准中不同光谱图像间存在较大的灰度差异、同一景物在不同波段表现的特征也各不相同等问题，提出了基于互信息相似性测度的多光谱图像配准算法。(In this paper, multi-spectral image registration between different spectral image there is a big difference in gray scale, the same scene in different bands performance characteristics are different questions, we present a similarity measure based on mutual information of multi-spectral image registration algorithm .)

近期成果，与大家分享：奇异值分解、小波降噪、zoom细化 谱峭度 和滤波器设计，共勉(Recent achievements to share with you: singular value decomposition, wavelet noise reduction, zoom refined spectral kurtosis and filter design, encouragement)

方向加速法（Powell）是一种直接法，不用计算目标函数的梯度，仅通过比较目标函数的数值大小来移动迭代点就可以求出极值。(Powell use for registration)

超分辨率中要使用的分块重叠及恢复，任意像素重合，超简单！(Super-resolution block overlap and recovery, to use in any pixel overlap, super easy!)

统计颜色直方图的matlab程序,解压缩后可以直接在MATLAB环境中运行(Color histogram statistics of the matlab program, unzip it directly in the MATLAB environment)

图像的自动拼接和缝补，采用sift算法作为匹配，是学习全景图像拼接的基础，大家可以下载下来看看(Automatic image stitching and sewing, using sift algorithm as a match, is the basis for learning panoramic image mosaic, you can download to see)

说明：  数学地角度来看，信号与图像处理可以统一看作是信号处理（图像可以看作是二维信号），在小波分析地许多分析的许多应用中，都可以归结为信号处理问题。对于其性质随时间是稳定不变的信号，处理的理想工具仍然是傅立叶分析。但是在实际应用中的绝大多数信号是非稳定的，而特别适用于非稳定信号的工具就是小波分析。(Mathematically, signal and image processing can be regarded as signal processing (image can be regarded as two-dimensional signal). In many applications of wavelet analysis, they can be reduced to signal processing. For signals whose properties are stable over time, the ideal tool for processing is still Fourier analysis. However, most of the signals in practical applications are unstable, and the tool especially suitable for unstable signals is wavelet analysis.)