目的 图像超分辨率算法在实际应用中有着较为广泛的需求和研究。然而传统基于样本的超分辨率算法均使用简单的图像梯度特征表征低分辨率图像块,这些特征难以有效地区分不同的低分辨率图像块。针对此问题,在传统基于样本超分辨率算法的基础上,提出双通道卷积神经网络学习低分辨率与高分辨率图像块相似度进行图像超分辨率的算法。方法 首先利用深度卷积神经网络学习得到有效的低分辨率与高分辨率图像块之间相似性度量,然后根据输入低分辨率图像块与高分辨率图像块字典基元的相似度重构出对应的高分辨率图像块。结果 本文算法在Set5和Set14数据集上放大3倍情况下分别取得了平均峰值信噪比(PSNR)为32.53 dB与29.17 dB的效果。结论 本文算法从低分辨率与高分辨率图像块相似度学习角度解决图像超分辨率问题,可以更好地保持结果图像中的边缘信息,减弱结果中的振铃现象。本文算法可以很好地适用于自然场景图像的超分辨率增强任务。

Objective All traditional example-based super-resolution methods adopt image-gradient features for low-resolution images and thus, these methods are unable to characterize the low-resolution space satisfactorily. To address this issue, this paper proposes a novel unified framework for image super-resolution that effectively combines example-based method with deep learning models. Method The proposed method consists of three main stages:low- and high-resolution similarity-learning, high-resolution patch-dictionary-learning, and high-resolution patch-generating stages. At the first stage, two different convolutional neural networks are proposed for learning a novel similarity metric between high- and low-resolution image patches. At the second stage, the high-resolution patch dictionaries are learned from training sets. At the last stage, the high-resolution patches are generated based on learned similarities between the input low-resolution patch and the atoms in the high-resolution patch dictionary. Result Experimental results on several commonly adopted datasets show that the proposed two-channel model quantitatively and qualitatively achieves improved performance compared with other methods. Conclusion The proposed two-channel model can preserve more detailed information and reduce ringing artifacts in the resulting images.