  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 \url{https://github.com/Zheng222/IMDN}.
