论文标题
带有深词典的图像超分辨率
Image Super-Resolution with Deep Dictionary
论文作者
论文摘要
自从Dong等人的第一个成功以来，基于深度学习的方法已在单像超分辨率领域占主导地位。这取代了使用深神经网络的传统基于稀疏编码方法的手工制作的图像处理步骤。与明确创建高/低分辨率词典的基于稀疏编码的方法相反，基于深度学习的方法中的词典被隐式地作为多种卷积的非线性组合被隐式获取。基于深度学习的方法的一个缺点是，它们的性能因与训练数据集（室外图像）不同的图像而降低。我们提出了一个具有深层词典（SRDD）的端到端超分辨率网络，在该网络中，高分辨率词典在不牺牲深度学习优势的情况下明确学习。广泛的实验表明，高分辨率词典的显式学习使网络更适合室外测试图像，同时保持内域测试图像的性能。
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.
