Research map / Image generation / Super-resolution

Diffusion-based super-resolution

Accelerate diffusion super-resolution with one-step distillation and residual shifting.

85 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2023–2026) · an approach in Super-resolution, keyword cluster “image super · attention · resolution diffusion”

State History Benchmarks Tried Baselines Checklist Preprints Who Export

How it got here

Most cited paper of each year

• 2023 · Implicit Diffusion Models for Continuous Super-Resolution (CVPR 2023 · 272 citations)
• 2024 · SinSR: Diffusion-Based Image Super-Resolution in a Single Step (CVPR 2024 · 167 citations)
• 2025 · Arbitrary-steps Image Super-resolution via Diffusion Inversion (CVPR 2025 · 45 citations)
• 2026 · Realism Control One-step Diffusion for Real-world Image Super Resolution (AAAI 2026 · 1 citation)

Earliest papers here

• Implicit Diffusion Models for Continuous Super-Resolution (CVPR 2023 · 272 citations)
• ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS 2023 · 75 citations)
• Guided Depth Super-Resolution by Deep Anisotropic Diffusion (CVPR 2023 · 58 citations)

Benchmark records

The best number each year that beat every earlier one, among papers of this approach reporting their own method. Numbers are extracted by AI from each paper's text; check the paper before citing.

What has been tried

44 papers here report 500 measured changes (a component added, removed or swapped, compared with a baseline): helped 74%, no clear effect 2%, hurt 23%, mixed 1%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

Baseline numbers that disagree

The same baseline on the same benchmark and metric, reported more than 5% apart by different papers here. Training budgets, data splits and reimplementations differ, so state which number you compare with.

What reviewers will expect

Datasets and baselines named in the 27 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Recent preprints

Who publishes on it

Institutions on the most papers of this approach since 2023 (of 85 papers; from OpenAlex and the papers' affiliations).

• Tsinghua University · 6 papers
• The Hong Kong Polytechnic University · 5 papers
• Shanghai Jiao Tong University · 5 papers
• Microsoft · 4 papers
• Saarland Informatics Campus · 4 papers
• Huawei · 3 papers
• Peking University · 3 papers
• Westlake University · 3 papers
• Alibaba Group · 2 papers
• Xidian University · 2 papers

Use it

Download BibTeX of 25 key papersOpen the topic in the interactive map

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Related approaches

Same keyword cluster

• Efficient lightweight super-resolution (101 papers)
• Perceptual and real-world SR (85 papers)
• CNN and transformer SR architectures (62 papers)

Elsewhere in the topic

• Real-world super-resolution (72 papers)
• Blind super-resolution (24 papers)

Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).