资源受限环境下实时超分辨重建方法研究

项目名称： 资源受限环境下实时超分辨重建方法研究

项目编号： No.61471161

项目类型： 面上项目

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： 张凯兵

作者单位： 西安工程大学

项目金额： 81万元

中文摘要： 实例学习是一种有效提高图像分辨率的超分辨重建技术。然而，受存储空间和计算能力的限制，很多实例学习超分辨算法由于学习结构复杂，计算密集度高，在实际中难以推广应用。因此，研究计算资源受限环境下的实时性超分辨重建算法，是该技术成功应用的关键。本课题拟研究结合基于实例学习和基于重构方法的快速单帧图像超分辨重建技术。提出一种基于结构非相关字典学习算法，获取表征低分辨与高分辨图像结构的多个线性特征子空间，通过学习低分辨与高分辨图像间多个线性映射关系，设计计算成本低、重建质量高的快速实例回归超分辨重建算法；同时，提出一种利用差值平方积分图技术的快速超分辨图像增强算法，将局部结构正则化、非局部相似性融合成一个统一的正则项，结合到基于重构超分辨框架下优化求解。为合理评判重建图像质量，进而评价重建算法的优劣，研究与人类主观感受相一致的无参考型图像质量评价准则，为超分辨重建算法的优化与参数选择提供可靠依据。

中文关键词： 实时性超分辨重建；结构非相关字典学习；多线性映射；实例回归；图像质量评价

英文摘要： Example learning has been recognized as an effective way to produce a high-resolution (HR) image from a single low-resolution (LR) input. However, due to the limitations of storage and computational capacity, many popular example learning-based super-resolution (SR) algorithms suffer from complicate learning structures and highly intensive computation, which makes them difficult to be applied in many practical applications. As a result, the key to successful applications of SR technique is to develop a fast yet effective algorithm that can be applied in most resource-limited scenarios. To this end, this proposal is going to make research on developing a fast single image SR approach that incorporates the advantages of both the example learning-based and reconstruction-based SR methods. In this proposal, a structured incoherence dictionary learning algorithm is proposed to represent multiple linearly coupled feature subspaces of LR and HR images. By establishing multiple linear mapping functions from the LR to HR images, a fast example regression-based SR algorithm is developed, which enables to maintain significantly low time and space complexity while making no compromise on quality. Furthermore, based on sum of squared difference (SSD), we propose a fast SR enhancement algorithm for improving the quality of SR images obtained by the example regression-based approach, wherein a unified regularization term that assembles local structure regularity and non-local similarity is integrated into the reconstruction-based SR framework for optimization. To fairly evaluate the quality of reconstructed results and the advantages and weaknesses of different SR methods, a no-reference image quality assessment metric for super-resolved images, which is consistent with the subjective perception, is going to be investigated, intending to provide a reliable evidence for the optimization of SR algorithms and the choice of related parameters.

英文关键词： Real-time super-resolution reconstruction;structured incoherence dictionary learning;multiple linear mapping;example regression;image quality assessment