Lightweight Image Super-Resolution with Adaptive Weighted Learning Network

Understand

Deep learning has been successfully applied to the single-image super-resolution (SISR) task with great performance in recent years.

• However, most convolutional neural network based SR models require heavy computation, which limit their real-world applications.
• In this work, a lightweight SR network, named Adaptive Weighted Super-Resolution Network (AWSRN), is proposed for SISR to address this issue.
• A novel local fusion block (LFB) is designed in AWSRN for efficient residual learning, which consists of stacked adaptive weighted residual units (AWRU) and a local residual fusion unit (LRFU).