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Found 4 papers, 2 papers with code
Self-Adaptive Reality-Guided Diffusion for Artifact-Free Super-Resolution
Following this, the Reality Guidance Refinement (RGR) process refines artifacts by integrating this mask with realistic latent representations, improving alignment with the original image.
Multi-Scale Implicit Transformer with Re-parameterize for Arbitrary-Scale Super-Resolution
MSSA further enhances the multi-scale characteristics of latent codes, resulting in better performance.
Efficient Mixed Transformer for Single Image Super-Resolution
Specifically, we propose the Mixed Transformer Block (MTB), consisting of multiple consecutive transformer layers, in some of which the Pixel Mixer (PM) is used to replace the Self-Attention (SA).
Image Super-Resolution using Efficient Striped Window Transformer
However, the challenge of balancing model performance and complexity has hindered their application in lightweight SR (LSR).
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