Efficient Model Agnostic Approach for Implicit Neural Representation
  Based Arbitrary-Scale Image Super-Resolution
Young Jae Oh Jihun Kim Tae Hyun Kim
Single image super-resolution (SISR) has experienced significant
advancements, primarily driven by deep convolutional networks. Traditional
networks, however, are limited to upscaling images to a fixed scale, leading to
the utilization of implicit neural functions for generating arbitrarily scaled
images. Nevertheless, these methodologies have imposed substantial
computational demands as they involve querying every target pixel to a single
resource-intensive decoder. In this paper, we introduce a novel and efficient
framework, the Mixture of Experts Implicit Super-Resolution (MoEISR), which
enables super-resolution at arbitrary scales with significantly increased
computational efficiency without sacrificing reconstruction quality. MoEISR
dynamically allocates the most suitable decoding expert to each pixel using a
lightweight mapper module, allowing experts with varying capacities to
reconstruct pixels across regions with diverse complexities. Our experiments
demonstrate that MoEISR successfully reduces up to 73% in floating point
operations (FLOPs) while delivering comparable or superior peak signal-to-noise
ratio (PSNR).