Transformer-based Selective Super-Resolution for Efficient Image
  Refinement
Tianyi Zhang Kishore Kasichainula Yaoxin Zhuo Baoxin Li Jae-sun Seo Yu Cao
Conventional super-resolution methods suffer from two drawbacks: substantial
computational cost in upscaling an entire large image, and the introduction of
extraneous or potentially detrimental information for downstream computer
vision tasks during the refinement of the background. To solve these issues, we
propose a novel transformer-based algorithm, Selective Super-Resolution (SSR),
which partitions images into non-overlapping tiles, selects tiles of interest
at various scales with a pyramid architecture, and exclusively reconstructs
these selected tiles with deep features. Experimental results on three datasets
demonstrate the efficiency and robust performance of our approach for
super-resolution. Compared to the state-of-the-art methods, the FID score is
reduced from 26.78 to 10.41 with 40% reduction in computation cost for the
BDD100K dataset. The source code is available at
https://github.com/destiny301/SSR.