In recent years, single image super-resolution (SISR) methods using deep
convolution neural network (CNN) have achieved impressive results. Thanks to
the powerful representation capabilities of the deep networks, numerous
previous ways can learn the complex non-linear mapping between low-resolution
(LR) image patches and their high-resolution (HR) versions. However, excessive
convolutions will limit the application of super-resolution technology in low
computing power devices. Besides, super-resolution of any arbitrary scale
factor is a critical issue in practical applications, which has not been well
solved in the previous approaches. To address these issues, we propose a
lightweight information multi-distillation network (IMDN) by constructing the
cascaded information multi-distillation blocks (IMDB), which contains
distillation and selective fusion parts. Specifically, the distillation module
extracts hierarchical features step-by-step, and fusion module aggregates them
according to the importance of candidate features, which is evaluated by the
proposed contrast-aware channel attention mechanism. To process real images
with any sizes, we develop an adaptive cropping strategy (ACS) to super-resolve
block-wise image patches using the same well-trained model. Extensive
experiments suggest that the proposed method performs favorably against the
state-of-the-art SR algorithms in term of visual quality, memory footprint, and
inference time. Code is available at https://github.com/Zheng222/IMDN.

该论文提出了一种轻量级信息多蒸馏网络（IMDN），通过构建包含蒸馏和选择性融合部分的级联信息多蒸馏块（IMDB），并使用自适应裁剪策略（ACS）来处理任何大小的图像，实现任意比例的超分辨率，同时利用中提出的对比度感知通道注意机制来评估候选特征的重要性，并将它们聚合起来。大量实验证明，该方法在视觉质量、内存占用和推理时间方面优于现有技术。