Since the first success of Dong et al., the deep-learning-based approach has become dominant in the field of single-image super-resolution. This replaces all the handcrafted image processing steps of traditional sparse-coding-based methods with a deep neural network. In contrast to sparse-coding-based methods, which explicitly create high/low-resolution dictionaries, the dictionaries in deep-learning-based methods are implicitly acquired as a nonlinear combination of multiple convolutions. One disadvantage of deep-learning-based methods is that their performance is degraded for images created differently from the training dataset (out-of-domain images). We propose an end-to-end super-resolution network with a deep dictionary (SRDD), where a high-resolution dictionary is explicitly learned without sacrificing the advantages of deep learning. Extensive experiments show that explicit learning of high-resolution dictionary makes the network more robust for out-of-domain test images while maintaining the performance of the in-domain test images.

翻译：自Dong等人首次成功以来,基于深层次学习的方法在单一图像超分辨率领域占据了主导地位,用深层神经网络取代了传统稀释编码方法的所有手工制作图像处理步骤。与基于稀释方法的方法相反,这种方法明确创建高/低分辨率词典,深层次学习方法的词典被隐含为多种融合的非线性组合。基于深层次学习方法的一个缺点是,其性能因与培训数据集(场外图像)不同的图像而退化。我们建议用深层词典(SRDD)建立一个端到端超分辨率网络,明确学习高分辨率词典,同时不牺牲深层学习的优势。广泛的实验表明,明确学习高分辨率词典使网络在保持内部测试图像的性能的同时对外部测试图像更加强大。