Single image super resolution aims to enhance image quality with respect to spatial content, which is a fundamental task in computer vision. In this work, we address the task of single frame super resolution with the presence of image degradation, e.g., blur, haze, or rain streaks. Due to the limitations of frame capturing and formation processes, image degradation is inevitable, and the artifacts would be exacerbated by super resolution methods. To address this problem, we propose a dual-branch convolutional neural network to extract base features and recovered features separately. The base features contain local and global information of the input image. On the other hand, the recovered features focus on the degraded regions and are used to remove the degradation. Those features are then fused through a recursive gate module to obtain sharp features for super resolution. By decomposing the feature extraction step into two task-independent streams, the dual-branch model can facilitate the training process by avoiding learning the mixed degradation all-in-one and thus enhance the final high-resolution prediction results. We evaluate the proposed method in three degradation scenarios. Experiments on these scenarios demonstrate that the proposed method performs more efficiently and favorably against the state-of-the-art approaches on benchmark datasets.

中文翻译：

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用于降级图像超分辨率的门控融合网络

单幅图像超分辨率旨在提高空间内容方面的图像质量，这是计算机视觉中的一项基本任务。在这项工作中，我们解决了存在图像退化（例如模糊、阴霾或雨条纹）的单帧超分辨率任务。由于帧捕获和形成过程的限制，图像劣化是不可避免的，并且超分辨率方法会加剧伪像。为了解决这个问题，我们提出了一种双分支卷积神经网络来分别提取基本特征和恢复特征。基本特征包含输入图像的局部和全局信息。另一方面，恢复的特征集中在退化区域并用于消除退化。然后通过递归门模块融合这些特征以获得超分辨率的清晰特征。通过将特征提取步骤分解为两个与任务无关的流，双分支模型可以通过避免一体学习混合退化来促进训练过程，从而增强最终的高分辨率预测结果。我们在三种退化场景中评估了所提出的方法。在这些场景上的实验表明，与基准数据集上的最新方法相比，所提出的方法更有效、更有利。双分支模型可以通过避免一体学习混合退化来促进训练过程，从而增强最终的高分辨率预测结果。我们在三种退化场景中评估了所提出的方法。在这些场景上的实验表明，与基准数据集上的最新方法相比，所提出的方法更有效、更有利。双分支模型可以通过避免一体学习混合退化来促进训练过程，从而增强最终的高分辨率预测结果。我们在三种退化场景中评估了所提出的方法。在这些场景上的实验表明，与基准数据集上的最新方法相比，所提出的方法更有效、更有利。