renlianjiance

对于水下地形海洋SAR探测有兴趣的读者可以来看看，对你会有帮助的。(2000，Two-dimensional topography inversion of underwater bottom by using SAR imagery.pdf)

彗星窗口水印源码例程程序结合易语言模块彗星窗口水印.ec，实现窗口水印效果。(Comets window watermark source routine program combining easy language module comet window watermark .ec, achieve window watermark .)

Understand state-of-the-art techniques for photorealistic rendering 􀂃 Become conversant with cutting edge graphics literature 􀂃 Hands-on exploration of one (or more) of the techniques encountered (a project). 􀂃 Learning and using raytracing to generate amazing pictures. 􀂃 Possibly extend one of the studied techniques, implement new ones

利用DOA和TOA对运动辐射源的单舰无源定位(Using a single passive location DOA and TOA ship moving emitter)

1.RAW格式转换为BMP格式 2.灰度线性拉伸 3.局部处理 4.几何处理 5.二值化 6.模板匹配 7.影像融合(1.RAW format converted to BMP format 2. grayscale linear stretch 3. local processing 4. geometric processing 5. binaryzation 6. template matching 7. image fusion)

最小二乘法拟合圆vc实现，最小二乘法(least squares analysis)是一种 数学 优化 技术，它通过 最小化 误差 的平方和找到一组数据的最佳 函数 匹配。 最小二乘法是用最简的方法求得一些绝对不可知的真值，而令误差平方之和为最小。(least squares fitting round vc realized, least-squares method (least squares analysis) is a mathematical optimization techniques, It error by minimizing the square and a group of data to find the best matching function. Least-squares method is the most simple method to derive absolutely true value unknown, and so the sum of squared errors to the minimum.)

压缩感知compressed sensing ,剑桥大学 教授，可以用于降噪、图像处理、文件压缩等等；(Cambridge- Compressive Sensing- Theory and Application；)

基于深度学习的图像超分辨率算法。 参考论文 Learning a Deep Convolutional Network for Image Super-Resolution (ECCV 2014) (Super resolution based on deep learning Refer to Learning a Deep Convolutional Network for Image Super-Resolution (ECCV 2014))

从一幅多个细胞的图像中，分割出单个的细胞，一个细胞一个小图。(more than one cell from the image, segmentation of individual cells, a cell a small map.)

利用四元数对彩色图像进行各种图像处理的，如滤波、快速傅立叶变化、图像质量评价等。(Use quaternion various images for color image processing, such as filtering, fast Fourier transform, the image quality evaluation.)