ImageRestore2

游程编码是是一种有效的信源编码，能够实现对二值化的图像或者二元序列进行压缩，且具有较好的压缩率。(this program is a good kind of RLC. It could come out rlc, and could compression binary sequences and image )

这些是我看过的一下关于三维成像的很不错的文章，有机载前视三维成像，还有用于大视场红外扫描的三维成像的文章，都很不错(These are some article about 3D imaging I have seen. Some is about SAR forword-looking，and some is about laser real-time 3D imaging。)

用奇异值分解的方法实现图像的去噪，并且与其他的方法进行比较，较之其他的方法有了明显的提升(To eliminate the noise from signals and compared to others mothods，an depressing effect is gotten)

tutorial压缩感知代码。压缩感知，又称压缩采样，压缩传感。它作为一个新的采样理论，它通过开发信号的稀疏特性，在远小于Nyquist 采样率的条件下，用随机采样获取信号的离散样本，然后通过非线性重建算法完美的重建信号(The tutorial compression-aware code. Compressed sensing, also known as compressed sampling, compressed sensing. It as a new sampling theory, through the development of signal sparsity, in conditions far less than the Nyquist sampling rate, using random sampling for discrete samples of the signal, and then through the non-linear reconstruction algorithm perfect reconstructed signal)

基于matlab的畸变校正（摄像机内外参数标定已知）(Based on the matlab the calibration)

图像处理标准测试图片库，用于数字图像处理各种仿真实验。(Standard test image gallery image processing to digital image processing all kinds of simulation experiments. )

4_1：生成模糊化实验图像 4_2：维纳滤波复原 4_3：约束最小二乘滤波复原 4_4：Lucy-Richardson滤波复原 4_5：盲卷积滤波复原(4_1: generate fuzzy images of experimental 4_2: 4_3 restoration Wiener filtering: constrained least squares filtering recovery 4_4: Lucy-Richardson filter recovery 4_5: Blind deconvolution filter to recover)

基于kscd算法的合成孔径雷达图像压缩。(Based on kscd algorithm synthetic aperture radar (SAR) image compression.)

非局域均值去噪算法，以及针对mri图片rician噪声的改进算法。(nonlocal mean denoising algorithm, and its modified version for mri images with rician noise.)

基于马尔科夫随机场的，例子学习超分辨率复原代码。(This is an implementation of the example-based super-resolution algorithm. Although the applications of MSFs have now extended beyond example-based super resolution and texture synthesis, it is still of great value to revisit this problem, especially to share the source code and examplar images with the research community. We hope that this software package can help to understand Markov random fields for low-level vision, and to create benchmark for super-resolution algorithms.)