一种保边缘影像超分辨率重建方法

An Edge-preserving Image Super-resolution Reconstruction Method

提出了一种基于方向性平滑测度的保边缘加权马尔可夫先验模型,并将其应用到基于最大后验估计的影像超分辨率重建中。该模型对邻域内不同方向的平滑测度使用不同的权值,以此减小对影像高频成分的惩罚约束,进而保护影像的边缘。利用不同影像对本文方法进行了验证,并用MSE影像评价方法对重建影像进行了定量评价。实验结果表明,与传统马尔可夫先验模型相比,加权马尔可夫先验模型能有效保护影像的边缘,取得更好的重建结果。

In this paper we introduce a new MAP-based super-resolution method, which can effectively preserve image edges by using an improved directional image prior model. The proposed image prior model, which we refer to as a weighted directional Markov image prior model, utilizes different weights for different directional smoothness measures of the edge pixels. Definitely, larger weights are chosen for smooth measures along the edge in order to penalize them to a larger extent and smaller weights are chosen for smooth measures across the edge for a less penalization. Thus, the edges of the reconstructed HR image can be effectively persevered. The proposed algorithm is tested on different series of images. The experimental results indicate that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.