Combining synthesis sparse with analysis sparse for single image super-resolution
Combining synthesis sparse with analysis sparse for single image super-resolution
复制标题
将合成稀疏与分析稀疏相结合实现单图像超分辨率
DOI：
10.1016/j.image.2020.115805
复制
发表时间：
2020-04-01
影响因子：
3.5
通讯作者：
Fu, Peng
中科院分区：
文献类型：
--
作者：
Li, Xuesong;Cao, Guo;Fu, Peng
基于稀疏编码的方法在单幅图像超分辨率(SR)中表现出了很好的效果。现有方法一般仅使用合成稀疏编码。然而，分析稀疏编码模型是综合稀疏编码模型的一种替代模型，而分析稀疏编码模型通常被忽略。本文将综合稀疏编码和分析稀疏编码相结合，提出了一种新的单幅图像随机共振方法。与现有的基于稀疏编码的随机共振方法相比，在低分辨率特征表示阶段，我们用分析稀疏编码代替了合成稀疏编码。因此，我们使用分析字典来获得LR系数，并使用合成字典来重建高分辨率(HR)块。由于引入了分析稀疏表示，用软阈值收缩代替了L(0)或L(1)的优化，这是一种更节省时间的方法。为了提高训练模型的收敛速度，我们引入了一个线性映射函数来揭示HR系数和LR系数之间的关系。采用交替优化策略对改进后的训练模型进行求解。此外，还考虑了全局约束和非局部约束，以提高重建图像的质量。实验结果表明，与已有的随机共振方法相比，本文提出的方法可以获得更好的性能。
Sparse coding based-methods show great effectiveness in single image super-resolution (SR). Existing methods generally use only synthesis sparse coding. However, the analysis sparse coding model, which is an alternative to the synthesis sparse coding model, is typically neglected In this paper, we propose a novel single image SR method by combining synthesis sparse coding with analysis sparse coding. In contrast to the existing sparse coding-based SR methods, we replace synthesis sparse coding with analysis sparse coding in the low-resolution (LR) feature representation phase. Thus, we use an analysis dictionary to obtain the LR coefficients and use a synthesis dictionary to reconstruct the high-resolution (HR) patches. Due to the introducing of analysis sparse representation, the l(0) or l(1) optimization is replaced by soft threshold shrinkage, which is a more time-saving method. To improve the convergence of the training model, we introduce a linear mapping function that reveals the relationship between the HR coefficients and LR coefficients. An alternating optimization strategy is adopted to solve the improved training model. Furthermore, global and nonlocal constraints are taken into account to improve the quality of the reconstructed images. Compared with some existing SR methods, the experimental results demonstrate that our proposed method can obtain better performance.