Scale-Aware Dynamic Network for Continuous-Scale Super-Resolution

Understand

Single-image super-resolution (SR) with fixed and discrete scale factors has achieved great progress due to the development of deep learning technology.

• However, the continuous-scale SR, which aims to use a single model to process arbitrary (integer or non-integer) scale factors, is still a challenging task.
• The existing SR models generally adopt static convolution to extract features, and thus unable to effectively perceive the change of scale factor, resulting in limited generalization performance on multi-scale SR tasks.
• Moreover, the existing continuous-scale upsampling modules do not make full use of multi-scale features and face problems such as checkerboard artifacts in the SR results and high computational complexity.