非局域样本填充和自适应曲率驱动模型的遥感图像修复算法

Remote Sensing Image Inpainting Based on Non-local Sample Filling and Adaptive Curvature Driven Diffusions Model

遥感图像修复技术对于后续遥感图像的处理与应用具有重要意义.文中在深入研究曲率驱动(CDD)模型和样本填充算法的基础上,针对遥感图像对纹理细节和边缘区域要求较高的特点,提出非局域样本填充和自适应曲率驱动模型的遥感图像修复算法.该算法较好地避免CDD模型修复过程中在一些极端情况下可能出现的假边缘、阶梯效应和扩散速度缓慢等缺点,保证遥感图像修复后的纹理细节信息和边缘信息.仿真实验验证文中算法的有效性.

Remote sensing image inpainting technology is significant for the following treatment and application of remote sensing image. Based on a thorough study of curvature driven diffusions （CDD） model and sample filling algorithm, a remote sensing image inpainting algorithm based on non-local sample filling and adaptive curvature driven diffusion model is proposed. The proposed algorithm can avoid the false edge, the staircase effect, the slow diffusion velocity, etc. in some extreme cases during the process ofimage inpainting. Meanwhile, it maintains the texture feature and edge information well for the inpainted image. The proposed algorithm is verified by simulation experiments.