摘要
针对红外图像分辨率低、视觉质量差等问题,提出基于局部约束群稀疏模型的红外图像超分辨率重建方法.考虑到红外图像的纹理自相似性和原子系数的群结构稀疏性,首先建立了基于局部约束的群稀疏表示模型.然后,在假定低分辨率图像空间和高分辨率图像空间具有相似流形的前提下,联合局部约束群稀疏表示模型和K-SVD(K奇异值分解)方法,训练得到高低分辨率图像对应的群结构字典对.最后,通过高分辨字典和对应的红外图像群稀疏表示系数重建得到高分辨率的红外图像.实验结果表明,本文方法具有更好的超分辨率效果,无论是在客观评价指标还是主观视觉效果方面都有明显的提高.
Aiming at the problems of low-resolution and poor visual quality of infrared images, a locality-constrained group sparsity based infrared image super-resolution algorithm is proposed. Firstly with considering the texture self-similarity of infrared images and group structural sparsity of atom coefficients, a locality-constrained group sparse （LCGS） model is proposed. Secondly, under LCGS and K-singular value decomposition, a pair of group structural dictionaries is learned. The dictionary pair can well capture and preserve the intrinsic geometrical manifold of low and high resolution data. Finally, the high-resolution infrared images are recovered by the high-resolution dictionary and the corresponding low- resolution group sparse coefficients. Experimental results show that the proposed method obtains excellent performance in objective evaluation and subjective visual effect.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2014年第4期144-151,共8页
Acta Physica Sinica
基金
国家自然科学基金(批准号:61162022
61362036)
江西省自然科学基金(批准号:20132BAB201021)
江西省科技落地计划(批准号:KJLD12098)
江西省教育厅科技项目(批准号:GJJ12632)资助的课题~~
关键词
红外图像
超分辨率
群稀疏
字典学习
infrared image, super-resolution, group sparse, dictionary learning
