摘要
随着稀疏编码与压缩传感理论的逐步发展,如何应用于图像的超分辨率成为研究热点之一.基于示例学习的算法,提出了一种新的超分辨率算法,其特点在于只基于低分辨率图像本身,没有额外的样本库,运用自然图像的自相似性与冗余性,学习低分辨率图像块与高分辨率图像块之间的函数关系.为了从图像中获取更加全面的信息,采用Guided滤波、一阶导数和二阶导数2种方法来提取特征.此外,提出了一种新的字典学习算法R-KSVD,并且改进了后项处理过程.实验结果显示,提出的算法具有较好的超分辨率效果和稳定性.
With the development of sparse coding and compressive sensing,image super-resolution reconstruction attracted extensive attentions.Based on the example-based algorithm,it was proposed a new super-resolution method.It exploited the relationship between the low image patches and the high image patches by the self-similarity of a natural image.The proposed method applied guided filter,the first-order and second-order derivatives to extract two kinds of features from the LR image,which was superior to using only one feature space.Besides,the effective dictionary was constructed by a novel algorithm called Relaxation K-SVD（R-KSVD）.Moreover,a new approach was proposed to estimate better HR residual image in the Back Projection.Experimental results demonstrated the superiority of the algorithm in both visual fidelity and numerical measures.
出处
《浙江师范大学学报（自然科学版）》
CAS
2013年第2期121-126,共6页
Journal of Zhejiang Normal University:Natural Sciences
基金
国家自然科学基金资助项目(61170109)
浙江省科技厅公益性应用研究计划项目(2012C21021)
关键词
超分辨率
稀疏编码
方向滤波
自相似性
super-resolution
sparse coding
guided filter
self-similarity
