基于压缩感知和局部纹理特性的超分辨率重建算法

Super Resolution Reconstruction Algorithm Based on Compressive Sensing and Local Texture Feature

将压缩感知理论应用于图像超分辨率重建技术,并针对总变分正则化方法未能考虑图像局部纹理特性的缺点,提出一种基于局部方向特性的总变分正则化函数.通过在图像的有界变差函数空间中构造与图像纹理方向一致的椭圆形区域,再利用最小化算法进行迭代求解获得去噪图像,可以达到更好地重建图像边缘信息的效果.实验结果表明,该算法可以有效降低图像的均方误差,提高峰值信噪比,并在重建过程中保持了图像边缘和纹理细节,提高了图像的对比度和清晰度.

In this paper compressive sensing theory is applied to image super-resolution reconstruction technique,and in order to overcome the disadvantage that the total variation regularization method without considering image＇s local texture feature,an adaptive total variation function based on local orientation features is proposed.Construct an ellipse region coincides with the dominant direction of the local image in the space of the bounded variation function.Then to obtain the denoised image by using iteration to optimize the minimum total variation.Experimental results show that the proposed algorithm can effectively reduce the mean square error of the image,improve the peak signal to noise ratio,and maintain the image edge and texture details in the reconstruction process,so as to improve the contrast and clarity of the image.