基于残差字典学习的图像超分辨率重建方法

为了提高图像重建质量,在保留图像空间结构信息的同时恢复更多图像高频信息,提出一种基于二维可分离字典和残差字典的图像超分辨率重建方法.不同于传统的基于一维字典的超分辨重建方法,二维字典直接利用图像的二维矩阵表示,因此,可以保持图像的空间结构信息,减少字典参数的数量,节省存储空间.为了更好地恢复图像高频信息,在二维可分离字典重建图像基础上,引入残差字典,重建边缘等高频信息,两类字典各有侧重,二者结合可得到更高质量的超分辨率重建图像.在典型的公共图像集上的实验证明了提出的结合二维可分离字典和残差字典的图像超分辨重建方法的有效性和优越性.

Image Super-resolution Reconstruction Based on Residual Dictionary Learning

In this paper, an image super-resolution method was proposed based on 2D separable dictionary and residual dictionary to improve the quality of reconstructed image and to preserve space information as well as high-frequency information. Unlike the conventional super-resolution method based on 1D dictionary, the 2D separable dictionary was constructed from a set of training images in 2D matrix form without vectorization, so it had the capacity of learning the inherent texture structure of images. Additionally, it was a pair of compact dictionaries that were of smaller size and less storage space compared with the conventional dictionary. To restore more high-frequency information, the residual dictionary was introduced based on the reconstructed images with the 2D separable dictionary, which captured the high-frequency information of edges, angles and corners in images. Combining the two dictionary learning procedures into one framework, the proposed method was expected to synthesize high resolution images with high quality. The proposed algorithm was tested on public natural image set. The experiment results show that the proposed image super-resolution method based on 2 D separable dictionary and residual dictionary is effective and superior.