| 摘 要: 针对Yang等人提出的基于稀疏表示的图像超分辨率的重建效果不够理想问题，提出了一种将图像卡通纹 理分解和稀疏表示相结合的方法用以实现单幅低分辨率图像的超分辨率重建。本文提出的算法涉及到卡通字典和纹理字 典的学习，图像重建过程分为两步。首先重建观测低分辨率图像的卡通高分辨率图像和纹理高分辨率图像，最后将重建 的卡通和纹理高分辨率图像线性加权叠加，实现低分辨率观测图像的超分辨率重建。实验结果表明，本文提出的方法在 主观视觉和客观指标峰值信噪比(PSNR)上都有明显的提升。 |
| 关键词: 超分辨率；稀疏表示；字典学习；卡通纹理 |
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中图分类号: TP391
文献标识码: A
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| 基金项目: 浙江省自然科学基金(项目编号:LY15F010007);国家自然科学基金(项目编号:61401399) |
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| Image Super-resolution Reconstruction Based on Cartoon-texture Decomposition and Sparse Representation |
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XU Chuan，DUANMU Chunjiang
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( College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321000, China)
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| Abstract: Due to the unsatisfactory result of image super-resolution reconstruction based on sparse representation method proposed by Yang et al,this paper proposes a new method,which combines image cartoon-texture decomposition and sparse representation,to achieve super-resolution reconstruction of low-resolution single images.The algorithm proposed in this paper involves two types of dictionaries:the cartoon dictionary and the texture dictionary.The image reconstruction process is divided into 2 steps:firstly,it reconstructs a high-resolution cartoon image and a high-resolution texture image from a low-resolution image,then,it overlays the newly reconstructed high-resolution cartoon image and the high-resolution texture image through linear weighting.As the experiment result shows,the method proposed in this paper brings significant improvement in both subjective visual quality and objective PSNR (Peak Signal to Noise Ratio). |
| Keywords: super resolution;sparse representation;dictionary learning;cartoon-texture |
