RawFormer: An Efficient Vision Transformer for Low-Light RAW Image Enhancement

Wanyan Xu, Xingbo Dong, Lan Ma, Andrew Beng Jin Teoh, Zhixian Lin

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Low-light image enhancement plays a central role in various downstream computer vision tasks. Vision Transformers (ViTs) have recently been adapted for low-level image processing and have achieved a promising performance. However, ViTs process images in a window- or patch-based manner, compromising their computational efficiency and long-range dependency. Additionally, existing ViTs process RGB images instead of RAW data from sensors, which is sub-optimal when it comes to utilizing the rich information from RAW data. We propose a fully end-to-end Conv-Transformer-based model, RawFormer, to directly utilize RAW data for low-light image enhancement. RawFormer has a structure similar to that of U-Net, but it is integrated with a thoughtfully designed Conv-Transformer Fusing (CTF) block. The CTF block combines local attention and transposed self-attention mechanisms in one module and reduces the computational overhead by adopting a transposed self-attention operation. Experiments demonstrate that RawFormer outperforms state-of-the-art models by a significant margin on low-light RAW image enhancement tasks.

Original languageEnglish
Pages (from-to)2677-2681
Number of pages5
JournalIEEE Signal Processing Letters
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 1994-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering


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