资源列表
SRCNN
- 通过卷积神经网络CNN实现超分辨率重建，利用训练模型实现参数和权重偏移的训练，达到输入低分辨率图像，输出高分辨率图像，测试例程附录。-Convolution neural network CNN to achieve super-resolution reconstruction using a training model parameters and to achieve weight shift training to achieve a low-resolution image inpu
QGA
- 利用量子遗传算法进行自适应图像增强。源码完整可直接使用。-Enhancing the image aotumatically by QGA
modulate_GIyanzheng_NGI
- 归一化鬼成像计算方法，比传统鬼成像算法更快更好-Normalized ghost imaging calculation method, faster and better than traditional ghost imaging algorithm
braintissue
- 对最大的连通区域依次进行膨胀，闭操作，填充空穴得到脑模板，再将原图和脑模板相乘得到脑组织图像；对距离函数取反，然后进行分水岭操作，显示每一个连通区域；对合并之后的连通区域进行闭操作去除分水线，然后将里面的空洞进行填充，再将其膨胀，减去填充后的模板便可得到各个结构的轮廓线-The maximum connected region sequentially inflated closing operation, fill the hole to give the brain a template,
MLRI_Demosaicing
- 利用最小拉普拉斯残差插值的去马赛克算法，性能很不错。-Laplace interpolation with a minimum residual demosaic algorithms, performance is very good.
rad_calforlandsat8
- Radiation calibration for landsat8 TM.批处理进行landsat8辐射校正程序，保证好用。简单方便。-Radiation calibration for landsat8 TM.Landsat8 radiation correction batch programs to ensure easy.Simple and convenient.
biomass_cal
- 利用植被指数进行生物量计算，批处理，直接输出结果图像。-Using vegetation indices for biomass calculation, batch processing, output images directly.
xt3
- 3、 请采用学过的图像复原算法（鼓励自己研究新算法），对 blur3.jpg、 sadhna_53_45.bmp和telescope.bmp 图像进行复原处理。-3, please use the learned image restoration algorithm (to encourage their study new algorithm), to blur3.jpg, sadhna_53_45.bmp and telescope.bmp image restoration proces
wrap-unwrap
- 相位解调和相位展开的matlab代码，可以从条纹图中提取出相位信息-Phase demodulation and phase unwrapping matlab code, can be extracted the fringe of the phase information
MRMRF
- 基于MRF图形的小波与分解 基于MRF图形的小波与分解 基于MRF图形的小波与分解-Wavelet and decomposition based on MRF graphWavelet and decomposition based on MRF graphWavelet and decomposition based on MRF graphWavelet and decomposition based on MRF graph
SiftRegistrationWithSCM
- 改进SIFT算法，用于光学图像和SAR图像配准-Improved SIFT methods for Registration of Optical and SAR image.
KDE
- 用MATLAB实现KDE算法，对20幅图像进行训练，用一幅图像进行测试-KDE algorithm using MATLAB to carry out training on 20 images, with an image for testing