基于超像素稀疏表示的图像超分辨率方法研究

项目名称： 基于超像素稀疏表示的图像超分辨率方法研究

项目编号： No.61461028

项目类型： 地区科学基金项目

立项/批准年度： 2015

项目学科： 无线电电子学、电信技术

项目作者： 刘微容

作者单位： 兰州理工大学

项目金额： 43万元

中文摘要： 超高清显示技术的快速发展必将对高空间分辨率的图像产生巨大需求，受成像工艺、成本和环境等因素制约，高空间分辨率的图像难以广泛获取，亟待研究能有效提升现有低质量图像空间分辨率的方法。虽然基于稀疏表示的图像超分辨率取得了一些研究成果，但是采用像素或图像块的稀疏表示方法对图像复杂结构表示不够准确，且过完备字典未能充分利用图像自身信息和样本图像先验信息。因此，基于稀疏表示的图像超分辨率方法仍有很大性能提升空间。 本项目将深入研究基于超像素稀疏表示的图像超分辨率方法，包括提出图像复杂结构的超像素表示方法，提出内容和尺度自适应的高-低分辨率超像素字典学习方法，建立高-低分辨率超像素间的关系模型，提出基于超像素的结构化稀疏表示模型和求解算法。突破像素或图像块等方式对图像复杂结构超分辨率的限制，精确重构低分辨率图像缺失的细节信息，提升图像超分辨率方法的性能，在低分辨率壁画图像的高清展示中取得实际应用。

中文关键词： 图像超分辨率；稀疏表示；超像素；多尺度分析

英文摘要： With the rapid developments of ultra-high definition display technologies in recent years，the demand for high spatial resolution images grows faster． It is important to reconstruct the high resolution image from the corresponding low-resolution image using digital image processing techniques, without updating the imaging equipment. Several image superresolution methods based on sparse representation have been proposed. However, the complex structures in an image can not be accurately represented by the traditional pixel-level or block-level sparse representation, and the prior information in the low resolution input image has not been used for over-complete dictionaries learning. To solve those problems, this project focuses on investigating image superresolution method via sparse representation with superpixel. Combining sparse representation theory with superpixel segmentation method and multiscale analysis theory, firstly, we will propose a new superpixel representation method of complex structures in an image. Secondly, we will present a new superpixel-level dictionary learning algorithm with content and scale adaptive. Thirdly, we will design a structural sparse representation model with superpixel, and a prior model between the high-resolution superpixel samples and low-resolution superpixel samples. Finally, we will propose a new image superresolution method via sparse representation with superpixel to accurately reconstruct fine details, and improve the robustness of image superresolution method. The achievements can be applied to superresolution of the low-resolution images from painted murals.

英文关键词： Image Superresolution;Sparse Representation;Superpixel;Multiscale Analysis