Opt Lett. 2018 Jul 15;43(14):3265-3268. doi: 10.1364/OL.43.003265.
To address the key image interpolation issue in microgrid polarimeters, we propose a machine learning model based on sparse representation. The sparsity and non-local self-similarity priors are used as regularization terms to enhance the stability of an interpolation model. Moreover, to make the best of the correlation among different polarization orientations, patches of different polarization channels are joined to learn adaptive sub-dictionary. Synthetic and real images are used to evaluate the interpolated performance. The experimental results demonstrate that our proposed method achieves state-of-the-art results in terms of quantitative measures and visual quality.
为了解决微电网偏振仪中的关键图像插值问题，我们提出了一种基于稀疏表示的机器学习模型。稀疏性和非局部自相似性先验被用作正则化项，以增强插值模型的稳定性。此外，为了充分利用不同偏振方向之间的相关性，将不同偏振通道的补丁拼接起来，以学习自适应子字典。合成图像和真实图像用于评估插值性能。实验结果表明，在定量指标和视觉质量方面，我们提出的方法达到了最新水平。
