Gimp-trainning

全部都是图像处理的最新文献，与激光雷达有关，利用激光与摄像头结合，实现主动驾驶(image processing)

实现image quilting算法进行纹理合成。(implement of Texture synthesis)

用OSTU和KSW实现多阈值图像分割，matlab代码(Image Segmentation Using OSTU and KSW)

简单背景下的红外目标检测，本程序用背景差分法实现目标检测，效果很好。(Simple under the background of the infrared target detection, the background difference method is used to implement target detection program, the effect is very good. )

Matlab在医学图像处理中的应用Study on Image Processing of the Lung Cancer and Bone by Using MATLAB (Study on Image Processing of the Lung Cancer and Bone by Using MATLAB )

刀刃法获取靶标图像的MTF,PSF.适用于遥感图像中的MTF提取。(The blade method for target images MTF, PSF. Apply to the remote sensing image MTF extraction.)

属于图像质量评价领域，无参考质量评价算法算法BIQI(Belongs to the field of image quality assessment, quality assessment algorithm without reference algorithm BIQI)

利用C++实现了BMP图像的读入和显示利用C++实现了BMP图像的读入和显示利用C++实现了BMP图像的读入和显示(Use C++ achieve the BMP image to read and display BMP images using the C++ achieve to read and display BMP images using the C++ achieve to read and display)

基于DCT域的鲁棒水印，能抗击一定程度的压缩，剪切等攻击(Based on the DCT domain robust watermarking, can fight against a certain degree of compression, shear, such as attacks)

提出了一种结合USFFT Curvelet 变换的各向异性扩散图像去噪模型。它有机结合了Curvelet 变换和各向 异性扩散（P-M扩散）两者的优点。通过P-范数方法选择合适的梯度阈值K，P-M扩散过程通过处理经过Curvelet 变换得到的图像的不同尺度的Curvelet 系数矩阵，实现了建立在对图像多尺度分析的基础上的新P-M扩散模型。 实验表明，新模型的处理结果能有效避免传统P-M 扩散出现的阶梯效应，同时更好地保留图像的纹理和细节。 关键词：图像去噪算法；各向异性扩散；Curvelet 变换；P-M 扩散(Abstract: An image de-noising model by integrating anisotropic diffusion with USFFT Curvelet transform was proposed, which combined the strongpoint of Curvelet transform with anisotropic diffusion (P-M diffusion). By choosing appropriate gradient threshold K through p-norms and carrying out the P-M diffusion process depend on the different scale matrixes of Curvelet coefficient of the image from Curvelet transform iterations, as a result, the improved model made it possible to carry out the new P-M diffusion de-noising process based on multi-scale analysis of the image. The experiment results have demonstrated that the model can avert the stair-casing effect in the traditional P-M diffusion effectively and keep the textures and details of images better.)