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相干光成像 菲涅尔衍射积分的S_FFT算法(S_FFT algorithm coherent light imaging Fresnel diffraction)

伪彩色增强的matlab程序，主要基于空间域灰度级－彩色变换,算法清晰,效果较好. (pseudo-color enhanced Matlab procedures, mainly on the spatial domain gray level-color transformation, the algorithm clear. effective.)

新的水下图像对比度增强和分割算法（实现对比度的增强和图像分割，内容包括：新的水下图像对比度增强算法、水下图像的分割算法--2008a）(The new underwater image contrast enhancement and segmentation algorithm (implemented contrast enhancement and image segmentation, including: new underwater image contrast enhancement algorithms, underwater image segmentation algorithm- 2008a))

这是用来做直方图匹配的工具，测试过可以实现(Histogram Matching Tool)

处理Gamma乘性噪声的PPB方法，考虑了非局部patch的相似性，可处理SAR图像去噪。(The PPB method for multicative noise removal, which based on the Probabilistic Patch similarity.)

说明：  《数字图像处理（第三版）》课后习题答案，作者冈萨雷斯(Answers to Exercises after Class of Digital Image Processing (Third Edition),author Gonzalez)

这个是INSAR图像处理的matlab工具箱，里面包含了很多有用的函数，对我们非常有帮助。(This is the INSAR image processing toolbox, which contains many useful functions, very helpful to us.)

实现SAR复数数据bin文件的读取、写入，可被ENVI、PolSARpro等软件读取(SAR complex data read and write)

说明：  基于cs的sar成像仿真，包括omp、sl0、osl0、onsl0等cs算法(ISAR imaging simulation based on CS, including OMP, sl0, osl0, onsl0 and other CS algorithms)

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.)