cszn/DnCNN

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1727 stars · 554 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DnCNN is the official implementation of the TIP 2017 paper 'Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising', providing deep residual CNN models for image denoising and related restoration tasks. It includes MatConvNet training/testing code with PyTorch and Keras/TensorFlow ports, and points to the newer PyTorch code in the KAIR repository.

Use cases

• remove gaussian noise from images with a deep cnn
• denoise images using residual learning networks
• jpeg deblocking artifact removal
• super-resolution of noisy images
• train a dncnn denoising model in pytorch
• use denoiser as plug-and-play prior for image restoration
• reproduce dncnn paper results

When to choose

• you need a well-known, heavily cited baseline for image denoising research
• you want pretrained models for gaussian denoising, srgb denoising, or jpeg deblocking
• you are implementing plug-and-play image restoration with a denoiser prior
• you want to study residual learning for low-level vision

When to avoid

• you need actively maintained production code - the author recommends the KAIR/DPIR repositories instead
• you need a license - the repository has no license, limiting reuse
• you want state-of-the-art denoisers - newer models like DRUNet, SwinIR, or SCUNet outperform it
• you need a simple drop-in library API rather than research scripts

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision computer-vision image-processing deep-learning machine-learning python image-denoising residual-learning cnn super-resolution jpeg-deblocking matconvnet pytorch keras tensorflow image-restoration research-code matlab gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="cszn/DnCNN")