一种改进Papoulis-Gerchberg的多幅超分辨重构方法

为进一步提高拍摄图像的分辨率,提出一种改进的Papoulis-Gerchberg超分辨算法.新算法提出边缘检测方法,可以改善传统方法空间复杂度和重构图像边缘模糊的问题.新算法在原有的算法基础上融于边缘检测,针对多幅同一场景输入图像,在每次Papoulis-Gerchberg迭代过程加入坎尼检测,同时将每步的重构误差投影到下一步重构过程,降低了算法空间复杂度,能有效恢复丢失的边缘高频信息.MATLAB实验结果表明,与现有的经典超分辨重构方法相比,本算法反映图像质量的峰值信噪比和灰度标准差更高,信噪比和灰度标准差比改进前算法分别提高0.5 d B和2.5.从视觉感官上对比,重构图像整体效果也更加清楚,去除了原始重构方法图像边缘叠影现象,有效提高了原始输入图像的分辨率.

An improved Papoulis-Gerchberg algorithm for multiframe super-resolution reconstruction

In order to enlarge a low resolution image clearly, an improved Papoulis-Gerchberg super-resolution method was proposed to solve the space complexity and the edge blurring phenomenon of reconstruction results. More specifically, the proposed algorithm uses edge detection operator, and canny detection is also joined in every Papoulis-Gerchberg iterative process, while reconstruction error is projected to next iterative process, such that the space complexity can be reduced and the lost high-frequency edge information can be recovered effectively. MATLAB experimental results show that the PSNR and the gray standard deviation improve 0.5 dB and 2.5, respectively, with comparison to the conventional Papoulis-Gerchberg method. Furthermore, the proposed algorithm can reconstruct multi-frame Low-Resolution images of same scene more accurately and the visual quality of the reconstruction image is clearer that the conventional one, and the proposed algorithm can also eliminate edge shadow and obtain a clear high-resolution image.