Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[ECCV 2020] Learning Enriched Features for Real Image Restoration and Enhancement. SOTA results for image denoising, super-resolution, and image enhancement.
| Date | Stars |
|---|---|
| 2026-07-24 | 723 |
| 2026-07-25 | 723 |
| 2026-07-28 | 724 |
| 2026-07-30 | 724 |
| 2026-08-06 | 724 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Learning Enriched Features for Real Image Restoration and Enhancement (ECCV 2020)
[Syed Waqas Zamir](https://scholar.google.ae/citations?hl=en&user=POoai-QAAAAJ), [Aditya Arora](https://adityac8.github.io/), [Salman Khan](https://salman-h-khan.github.io/), [Munawar Hayat](https://scholar.google.com/citations?user=Mx8MbWYAAAAJ&hl=en), [Fahad Shahbaz Khan](https://scholar.google.es/citations?user=zvaeYnUAAAAJ&hl=en), [Ming-Hsuan Yang](https://scholar.google.com/citations?user=p9-ohHsAAAAJ&hl=en), [Ling Shao](https://scholar.google.com/citations?user=z84rLjoAAAAJ&hl=en)
[](https://arxiv.org/abs/2003.06792)
[](https://drive.google.com/file/d/1QIKp7h7Rd85odaS6bDoeDGXb0VLKo8I9/view?usp=sharing)
[](https://www.youtube.com/watch?v=6xSzRjAodv4)
[](https://drive.google.com/file/d/1hnhqSrjqQQiYn7XPAGpFgMBTfBlb1QAy/view?usp=sharing)
<hr />
### News
- A lightweight, fast and extended version of MIRNet is accepted in **TPAMI**. [Paper](https://www.waqaszamir.com/publication/zamir-2022-mirnetv2/) | [Code](https://github.com/swz30/MIRNetv2)
- Keras Tutorial on MIRNet is available at https://keras.io/examples/vision/mirnet/
- Video on Tensorflow Youtube channel https://youtu.be/BMza5yrwZ9s
- Links to (unofficial) implementations are added [here](#other-implementations)
<hr />
> **Abstract:** *With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing. Recently, convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. Existing CNN-based methods typically operate either on full-resolution or on progressively low-resolution representations. In the former case, spatially precise but contextually less robust results are achieved, while in the latter case, semantically reliable but spatially less accurate outputs are generated. In this paper, we present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network, and receiving strong contextual information from the low-resolution representations. The core of our approach is a multi-scale residual block containing several key elements: (a) parallel multi-resolution convolution streams for extracting multi-scale features, (b) information exchange across the multi-resolution streams, (c) spatial and channel attention mechanisms for capturing contextual information, and (d) attention based multi-scale feature aggregation. In the nutshell, our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Extensive experiments on five real image benchmark datasets demonstrate that our method, named as MIRNet, achieves state-of-the-art results for a variety of image processing tasks, including image denoising, super-resolution and image enhancement.*
<details>
<summary> <strong>Network Architecture</strong> (click to expand) </summary>
<p align="center">
<img src = "https://i.imgur.com/vmywppl.png" width="700">
<br/>
<b> Overall Framework of MIRNet </b>
</p>
<table>
<tr>
<td> <img src = "https://i.imgur.com/tqpje3M.png" width="600"> </td>
<td> <img src = "https://i.imgur.com/DQ6SYaH.png" width="300"> </td>
</tr>
<tr>
<td><p align="center"><b>Selective Kernel Feature Fusion (SKFF)</b></p></td>
<td><p align="center"> <b>Downsampling Module</b></p></td>
</tr>
</table>
<table>
<tr>
<td> <img src = "https://i.imgur.com/FmHQ0VD.png" width="600"> </td>
<td> <img src = "https://i.iExcerpt of 10,083 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5e197a1df0cb78c8, topic:super-resolution, desc:super resolution, readme:super resolution
matched fp:5e197a1df0cb78c8, topic:pytorch
matched fp:5e197a1df0cb78c8, topic:computer-vision
