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Image Super-resolution
Taming Transformers for High-Resolution Image Synthesis
Published:12/18/2020
Generative Adversarial Policy OptimizationDiffusion ModelsImage Super-resolutionImage Synthesis
This study combines CNN's inductive bias with Transformer expressivity to synthesize highresolution images. It first learns a contextrich vocabulary of image constituents with CNNs, then models their composition using Transformers, achieving stateoftheart results in semantic
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Effective Diffusion Transformer Architecture for Image Super-Resolution
Published:9/29/2024
Diffusion ModelsImage Super-resolutionDiffusion TransformerMulti-Scale Hierarchical Feature ExtractionFrequency-Adaptive Time-Step Conditioning Module
DiTSR introduces a Ushaped diffusion transformer with frequencyadaptive conditioning, enhancing multiscale feature extraction and resource allocation, achieving superior superresolution without pretraining compared to priorbased methods.
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CiaoSR: Continuous Implicit Attention-in-Attention Network for
Arbitrary-Scale Image Super-Resolution
Published:12/8/2022
Image Super-resolutionContinuous Implicit Representation LearningAttention Mechanism NetworkArbitrary-Scale Super-ResolutionNon-local Feature Fusion
CiaoSR introduces an implicit attentioninattention network that adaptively weights local features and incorporates scaleaware attention, achieving stateoftheart performance in arbitraryscale image superresolution with strong generalization and flexibility.
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LinearSR: Unlocking Linear Attention for Stable and Efficient Image
Super-Resolution
Published:10/10/2025
Image Super-resolutionLinear Attention MechanismPerception-Distortion Trade-off OptimizationEarly-Stopping Guided Fine-tuningSNR-based Mixture of Experts
LinearSR enables stable, efficient image superresolution by addressing training instability, perceptiondistortion tradeoffs, and guidance efficiency using novel finetuning, SNRbased experts, and lightweight guidance strategies.
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