Single image super-resolution (SISR) has witnessed substantial progress recently by deep learning-based methods, due to the data-driven end-to-end training. However, most existing DL-based models are built intuitively, with little thought on priors. And the lack of interpretability limits their further improvements. To avoid this, this paper presents an end-to-end trainable unfolding network which leverages both DL- and prior-based methods. Specifically, we introduce the reweighted algorithm into CSC model and solve it by learning weighted iterative soft thresholding algorithm in a convolutional manner. Based on this, we present a SISR model by learning weighted convolutional sparse coding, in which the channel attention is resorted to learn the weight. Extensive experiments demonstrate the superiority of our method to recent state-of-the-art SISR methods, in terms of both quantitative and qualitative results.

由于数据驱动的端到端培训，单图像超分辨率（SISR）最近通过基于深度学习的方法目睹了巨大的进步。但是，大多数现有的基于DL的模型都是凭直觉构建的，很少考虑先验条件。而且缺乏可解释性限制了它们的进一步改进。为避免这种情况，本文提出了一种端到端可训练的展开网络，该网络同时利用了基于DL和基于先验的方法。具体来说，我们将重加权算法引入CSC模型，并通过卷积方式学习加权迭代软阈值算法来解决。在此基础上，我们通过学习加权卷积稀疏编码提出了一种SISR模型，在该模型中要利用信道注意力来学习权重。