# YapengTian/Single-Image-Super-Resolution

A collection of high-impact and state-of-the-art SR methods

Repository: https://github.com/YapengTian/Single-Image-Super-Resolution
Canonical: https://ross.abutalabs.com/products/single-image-super-resolution
Homepage: https://yapengtian.github.io/Single-Image-Super-Resolution/
License Family: other
Last push: 2023-06-20T16:54:31+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3615, "days_push": 1170, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1895, forks 377 (observed 2026-08-28T04:05:50.840181+00:00)

## What it is
A curated awesome-list of papers and resources on example-based single image super-resolution (SISR), spanning early learning-based, sparsity-based, and deep learning methods. It is a reference collection rather than runnable software.

## Use cases
- find state-of-the-art super-resolution papers
- learn about single image super-resolution as a beginner
- survey deep learning methods for image upscaling
- find code implementations of SISR algorithms
- research sparse coding based image restoration

## When to choose
- you need a curated reading list of super-resolution research
- you are starting out in image super-resolution and want foundational papers
- you want links to papers and code for classic and modern SR methods

## When to avoid
- you need ready-to-run super-resolution software or a library
- you need video or multi-image super-resolution resources
- you need a maintained tool rather than a paper list

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: image-processing, machine-learning, deep-learning
- domain: computer-vision, image-processing, machine-learning, tutorials
- platform: cross-platform
- tags: awesome-list, super-resolution, paper-collection, computer-vision, research-papers

## Member repositories
- YapengTian/Single-Image-Super-Resolution (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:50.840181+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:12:50.920567+00:00, confidence not recorded.
  - readme: https://github.com/YapengTian/Single-Image-Super-Resolution (fetched 2026-08-28T04:05:50.840181+00:00, sha d38d1d29b99f)
  - homepage: https://yapengtian.github.io/Single-Image-Super-Resolution/ (fetched 2026-08-29T10:52:03.701718+00:00, sha 137986365400)
- Data as of 2026-08-30T08:39:29.467469+00:00.