DnCNN
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Harbin Institute of Technology,Hong Kong Polytechnic University,ULSee Inc.,Xi’an Jiaotong University
- Organisation type
- Academia,Academia,Industry,Academia
- Country
- China, Hong Kong
- Published
- 1 February 2017
- Authors
- Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, Lei Zhang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Image generation
- Task
- Image super-resolution
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 2,560,000,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Unknown
- Citations
- 7,779
Sources
Where this record came from and when it was last checked.
- Reference
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
- Last updated
- 1 January 2026
What the numbers mean
What this model is
DnCNN was published by Harbin Institute of Technology,Hong Kong Polytechnic University,ULSee Inc.,Xi’an Jiaotong University, in China, in February 2017. academia,Academia,Industry,Academia is the category the publisher falls under.
It works in Vision, Image generation, and is recorded as doing image super-resolution.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
The training set ran to roughly 2,560,000,000 tokens.
The reason it appears in this catalogue at all is highly cited.
Answers
DnCNN — common questions
Is DnCNN open source?
The licensing for DnCNN was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does DnCNN have?
No parameter count has been published for DnCNN, which is why no memory or speed figure appears on this page.
Who created DnCNN?
DnCNN was published by Harbin Institute of Technology,Hong Kong Polytechnic University,ULSee Inc.,Xi’an Jiaotong University, based in China, categorised as academia,Academia,Industry,Academia.
When was DnCNN released?
DnCNN was published in February 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is DnCNN used for?
DnCNN works in Vision, Image generation, and is recorded as handling image super-resolution. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run DnCNN?
None. DnCNN is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.