基于残差字典及协作表达的单图像超分辨率算法

Single Image Super-resolution Algorithm Using Residual Dictionary and Collaborative Representation

通过传统的单图像超分辨率(Super Resolution,SR)算法重建的高分辨率图像往往存在高频信息不足、边缘模糊的问题。为了提升重建图像的质量,提出了一种基于残差字典及协作表达的单图像SR算法(Residual Dictionary and Collaborative Representation,RDCR)。在训练环节,该算法结合字典学习及协作表达的思想,首先训练一个主字典及主投影矩阵,其次利用重建的样本图像训练多层残差字典及多层残差投影矩阵;在测试环节,通过逐层重建残差信息,得到不断精细化的高频信息,以提升重建的高分辨率图像的质量。通过实验证明,相比传统算法A+,所提算法在4倍上采样下的Set5及Set14图像集上可以分别获得0.20 dB及0.18 dB的峰值信噪比增益,在运算时间上所提算法与A+接近。

Usually,the traditional single image super resolution(SR)algorithms generate the high resolution(HR)images with insufficient high-frequency information and blurred edges.To improve the quality of the reconstructed HR images,this paper proposes a single image SR algorithm by using residual dictionary and collaborative representation(Residual Dictionary and Collaborative Representation,RDCR).In the training phase,firstly,based on the ideas of dictionary learning and collaborative representation,a main dictionary and the corresponding main projection matrices are learned.After that,the reconstructed image samples are utilized to train multiple layers of residual dictionaries and residual projection matrices.In the testing phase,high-frequency information is gradually refined by reconstructing the residual information layer by layer.Extensive experimental results show that,at a scale factor of 4,the average peak signal-to-noise ratio(PSNR)values obtained by the proposed method on Set5 and Set14 are 0.20 dB and 0.18 dB higher than the traditional method A+,respectively.And the running time of the proposed method is close to that of A+.