Skip to main content
Restormer: Efficient transformer for High-resolution image restoration
  • Syed Waqas Zamir, Inception Institute of Artificial Intelligence
  • Aditya Arora, Inception Institute of Artificial Intelligence
  • Salman Khan, Mohamed bin Zayed University of Artificial Intelligence
  • Munawar Hayat, Monash University
  • Fahad Shahbaz Khan, Mohamed bin Zayed University of Artificial Intelligence
  • Ming-Hsuan Yang, University of California & Yonsei University & Google Research
Document Type

Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks. Recently, another class of neural architectures, Transformers, have shown significant performance gains on natural language and high-level vision tasks. While the Transformer model mitigates the shortcomings of CNNs (i.e., limited receptive field and in-adaptability to input content), its computational complexity grows quadratically with the spatial resolution, therefore making it infeasible to apply to most image restoration tasks involving high-resolution images. In this work, we propose an efficient Transformer model by making several key designs in the building blocks (multi-head attention and feed-forward network) such that it can capture long-range pixel interactions, while still remaining applicable to large images. Our model, named Restoration Transformer (Restormer), achieves state-of-the-art results on several image restoration tasks, including image deraining, single-image motion deblurring, defocus deblurring (single-image and dual-pixel data), and image denoising (Gaussian grayscale/color denoising, and real image denoising). The source code and pre-trained models are available at © 2021, CC BY-NC-SA.

Publication Date
  • Computer Vision and Pattern Recognition (cs.CV)

Preprint: arXiv

Archived with thanks to arXiv

Preprint License: CC BY-NC-SA 4.0

Uploaded 24 March 2022

Citation Information
S. Waqas Zamir, A. Arora, S. Khan, M. Hayat, F. Shahbaz Khan, and M.-H. Yang, "Restormer: Efficient transformer for High-resolution image restoration", 2021, arXiv:2111.09881