Create README.md

Author: swz30

README.md

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+# Learning Enriched Features for Fast Image Restoration and Enhancement (TPAMI 2022)
+
+[Syed Waqas Zamir](https://scholar.google.es/citations?user=WNGPkVQAAAAJ&hl=en), [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), and [Ling Shao](https://scholar.google.com/citations?user=z84rLjoAAAAJ&hl=en)
+
+[![paper](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2111.09881)
+
+
+
+# Code will be released before April 20. 
+
+<hr />
+
+> **Abstract:** *Given a degraded input image, image restoration aims to recover the missing high-quality image content. Numerous
+applications demand effective image restoration, e.g., computational photography, surveillance, autonomous vehicles, and remote
+sensing. Significant advances in image restoration have been made in recent years, dominated by convolutional neural networks
+(CNNs). The widely-used CNN-based methods typically operate either on full-resolution or on progressively low-resolution
+representations. In the former case, spatial details are preserved but the contextual information cannot be precisely encoded. In the
+latter case, generated outputs are semantically reliable but spatially less accurate. This paper presents a new architecture with a
+holistic goal of maintaining spatially-precise high-resolution representations through the entire network, and receiving complementary
+contextual information from the low-resolution representations. The core of our approach is a multi-scale residual block containing the
+following key elements: (a) parallel multi-resolution convolution streams for extracting multi-scale features, (b) information exchange
+across the multi-resolution streams, (c) non-local attention mechanism for capturing contextual information, and (d) attention based
+multi-scale feature aggregation. 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 six real image benchmark
+datasets demonstrate that our method, named as MIRNet-v2 , achieves state-of-the-art results for a variety of image processing tasks,
+including defocus deblurring, image denoising, super-resolution, and image enhancement.* 
+<hr />
+
+
+## Citation
+If you use MIRNet_v2, please consider citing:
+
+    @article{Zamir2022MIRNetv2,
+        title={Restormer: Efficient Transformer for High-Resolution Image Restoration}, 
+        author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat 
+                and Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao},
+        journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
+        year={2022}
+    }
+
+
+## Contact
+Should you have any question, please contact waqas.zamir@inceptioniai.org
+
+
+**Acknowledgment:** This code is based on the [BasicSR](https://github.com/xinntao/BasicSR) toolbox. 
+
+## Our Related Works
+- Restormer: Efficient Transformer for High-Resolution Image Restoration, CVPR 2022. [Paper](https://arxiv.org/abs/2111.09881) | [Code](https://github.com/swz30/Restormer)
+- Multi-Stage Progressive Image Restoration, CVPR 2021. [Paper](https://arxiv.org/abs/2102.02808) | [Code](https://github.com/swz30/MPRNet)
+- Learning Enriched Features for Real Image Restoration and Enhancement, ECCV 2020. [Paper](https://arxiv.org/abs/2003.06792) | [Code](https://github.com/swz30/MIRNet)
+- CycleISP: Real Image Restoration via Improved Data Synthesis, CVPR 2020. [Paper](https://arxiv.org/abs/2003.07761) | [Code](https://github.com/swz30/CycleISP)