Zhang Junchao
Appl Opt. 2020 Oct 1;59(28):8588-8594. doi: 10.1364/AO.397652.
An image interpolation model based on sparse representation is proposed. Two widely used priors including sparsity and nonlocal self-similarity are used as the regularization terms to boost the performance of the interpolation model. Meanwhile, we incorporate nonlocal linear regression into this model, since nonlocal similar patches could provide a better approximation to a given patch. Moreover, we propose a new approach to learn an adaptive sub-dictionary online instead of clustering. For each patch, similar patches are grouped to learn the adaptive sub-dictionary, generating a more sparse and accurate representation. Finally, weighted encoding is introduced to suppress tailing of fitting residuals in data fidelity. Abundant experimental results show that our proposed method achieves better performance compared to several state-of-the-art methods in terms of subjective and objective evaluations.
提出了一种基于稀疏表示的图像插值模型。使用稀疏性和非局部自相似性这两种广泛使用的先验作为正则化项，以提高插值模型的性能。同时，我们将非局部线性回归纳入该模型，因为非局部相似块可以为给定块提供更好的近似。此外，我们提出了一种在线学习自适应子字典而不是聚类的新方法。对于每个块，将相似块分组以学习自适应子字典，从而生成更稀疏和准确的表示。最后，引入加权编码以抑制数据保真度中拟合残差的拖尾。大量实验结果表明，在主观和客观评估方面，我们提出的方法与几种现有最先进方法相比具有更好的性能。
