基于显著性疏表示的图像超分辨率算法简

提出了一种全新的基于视觉显著度和上下文稀疏分解的图像超分辨率算法。利用人眼视觉感知显著的区域往往趋向于高度结构化的特性，字典学习和稀疏分解过程中可以捕获更多细节特征。在字典学习部分，视觉显著区域提取出的图像样本用来训练显著字典。在先验模型的部分，由于视觉显著区域通常趋于高度结构化，基于上下文的稀疏分解被用来进一步探索相邻图像块之间的联系。实验结果表明，所提出的方法在性能上优于其他最新的方法，峰值信噪比（PSNR）增益最大。主观结果也显示，所提出的方法可以有效减少假影现象，并保持更多细节。

Image super resolution based on salient sparse coding

We propose a novel image super-resolution method using salient sparse coding.Based on the common sense that human visual system is more sensitive to edges and structural information,we can infer that perceptually salient regions tend to be highly structured.Thus we utilize this property to capture more details during the dictionary learning and sparse coding process.When training dictionaries,image samples extracted from the salient regions are used to generate salient dictionaries.With regard to the prior model,context-aware sparse coding is incorporated to model the relationship between dictionary atoms of adjacent patches, especially in the salient regions.Experiments demonstrate the superiority of the proposed method to other state-of-the-art meth-ods with the highest PSNR gain.Subjective results also reveal that the proposed method reduces artifacts and preserves more de-tails.