swz30/MIRNet
A CNN-based architecture for real-world image restoration and enhancement tasks including denoising, super-resolution, and low-level vision.
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MIRNet (Multi-stage Image Restoration Network) learns enriched feature representations through multi-resolution streams and attention mechanisms to recover high-quality image content from degraded inputs. The model processes images through parallel feature extraction branches at different scales, combining them via selective fusion to preserve spatial details while enhancing contextual information. It achieves state-of-the-art results on benchmark datasets for image denoising, super-resolution, and general image enhancement tasks.
Frequently asked
- What is swz30/MIRNet?
- A CNN-based architecture for real-world image restoration and enhancement tasks including denoising, super-resolution, and low-level vision.
- Is MIRNet open source?
- Yes — swz30/MIRNet is an open-source project tracked on heatdrop.
- What language is MIRNet written in?
- swz30/MIRNet is primarily written in Python.
- How popular is MIRNet?
- swz30/MIRNet has 723 stars on GitHub.
- Where can I find MIRNet?
- swz30/MIRNet is on GitHub at https://github.com/swz30/MIRNet.