cvprcode

基于自适应的区域选择和自适应的正则化得图像去模糊和超分辨处理，试过可以用(Based adaptive region selection and adaptive regularization was image deblurring and super-resolution processing)

一些常用的图像处理评价指标，处理后图像对比度，边缘剩余数，处理前后图像的相似性....(Some commonly used image processing evaluation, contrast, edges remaining number of similarities ....)

rk3399硬件编解码demo及文档

本文档主要用于图像的倾斜矫正，通过建立透视变换矩阵和强角点检测实现畸变图形的矫正(This document is mainly used for Image tilt correction, through the establishment of perspective transformation matrix and strong corner detection, to achieve distortion correction)

首先生成如下形式的图片：图中的白条为7象素宽，210象素高，白条间的宽度为17象素。 应用多种滤波器进行处理：算术均值，几何均值，谐波均值，逆谐波均值，中值滤波，中点滤波，最大值、最小值滤波等。注意，需改变模板大小。详细比较上述的结果，并分析原因。 (First generation following form images: figure IOUs 7 pixels wide, 210 pixels high, the width is 17 pixels between IOUs. Using a variety of filters for processing: the arithmetic mean, geometric mean, harmonic mean, inverse harmonic mean, median filtering, filtering midpoint, maximum, minimum filtering. Note that the need to change the template size. A detailed comparison of these results and analyze the reasons.)

一份retinex算法的code，其中利用pca 做 色度分解，利用改进的bilateral滤波器进行低频和高频分解(Retinex algorithm in a code, which made use of PCA colority decomposition, the use of improved bilateral filter for low frequency and high-frequency decomposition)

被动导引头辐射源跟踪新算法研究：粒子滤波+UKF的跟踪方法文献(The passive seeker radiation source tracking the new algorithm: particle filter tracking methods literature+UKF)

此matlab程序属于雷达信号处理领域，用于检测目标和对目标测速。(This matlab program belongs to the field of radar signal processing for detecting the target and the target speed.)

一种可适用于复杂光照环境的低照度图像增强算法，即基于内容感知的自适应对比度提高的增强算法。引入了改进的伽马矫正，可以根据低照度图像的明暗程度自适应的对图像进行预处理。在实验过程中还发现，虽然改进算法有效避免引入大量噪声，但仍有少量噪声存在，为了达到更好的视觉效果，利用改进的双边滤波对增强的图像进行处理，获得最后的增强图像。实验效果显示，该算法可对低照度图像实现理想的增强效果。(A can be applied to complex light environment of low illumination image enhancement algorithm, which based on the content of perception adaptive contrast enhancement algorithm. Introduced the improvement of gamma correction, can according to the low illumination image of light and shade degree adaptive to image preprocessing. In the process of experiment also found that although the improved algorithm effectively avoid introducing a lot of noise, but there still have a small amount of noise, in order to achieve better visual effect, use of improved bilateral filtering to enhance the image processing, finally enhance image. The experimental results showed that the algorithm can achieve a low illumination image enhancement effect. )

这本书提供了充分的代表性逆合成孔径雷达（ISAR）成像，这是一种流行和重要的雷达信号处理工具。本书涵盖了所有可能的ISAR成像方面。这本书提供了一个介绍了专家组的逆问题和合成孔径雷达（SAR）提出的问题之前，信号处理技术和雷达基本相当。如高分辨率SAR的重要概念，脉冲压缩和图像形成连同相关的MATLAB代码。 ISAR成像的基本原理后，这本书提供了相关的MATLAB函数和代码成像ISAR成像的详细程序。 ISAR成像，多个成像技巧和微调，如零填充和窗口程序，以提高图像的质量也被提出。最后，逆合成孔径雷达图像中的各种实际应用，如成像天线平台散射的，是在单独的一章。所有这些算法，MATLAB代码和数字都包括在内。最后一章考虑了在ISAR成像的先进理念和发展趋势。(This book provides a full representation of Inverse Synthetic Aperture Radar (ISAR) imagery, which is a popular and important radar signal processing tool. The book covers all possible aspects of ISAR imaging. The book offers a fair amount of signal processing techniques and radar basics before introducing the inverse problem of ISAR and the forward problem of Synthetic Aperture Radar (SAR). Important concepts of SAR such as resolution, pulse compression and image formation are given together with associated MATLAB codes. After providing the fundamentals for ISAR imaging, the book gives the detailed imaging procedures for ISAR imaging with associated MATLAB functions and codes. To enhance the image quality in ISAR imaging, several imaging tricks and fine-tuning procedures such as zero-padding and windowing are also presented. Finally, various real applications of ISAR imagery, like imaging the antenna-platform scattering, are given in a separate chapter. For all these algorithms, MATLA)