Dynamic Implicit Image Function for Efficient Arbitrary-Scale Super-Resolution

Implicit Neural Representation (INR)-based methods have achieved remarkable success in Arbitrary-Scale Super-Resolution (ASSR). However, these continuous image representations, where a decoder infers pixel values across a continuous spatial domain, suffer from rapidly increasing computational cost as the scale factor increases, limiting the practical applications of ASSR. To address this problem, we propose a Dynamic Implicit Image Function (DIIF) for efficient ASSR. Instead of independently using each image coordinate and its nearby 2D features as decoder inputs, DIIF introduces a coordinate grouping and slicing strategy to decode pixel value slices from coordinate slices. To perform efficient arbitrary-scale decoding, we further introduce a dynamic coordinate slicing strategy empowered by our Coarse-to-Fine MLP (C2F-MLP), which allows adjusting the number of coordinates in each slice as the scale factor varies. Extensive experiments demonstrate that DIIF can seamlessly integrate with INR-based ASSR methods, significantly reducing computational cost and runtime, while maintaining State-Of-The-Art (SOTA) SR performance.