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WeatherMAR: Complementary Masking of Paired Tokens for Adverse-Weather Image Restoration.

Junyuan Ma1,2,3, Qunbo Lv1,2,3, Zheng Tan1,2,3

  • 1Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China.

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Summary

WeatherMAR is a novel framework for multi-weather image restoration. It effectively restores images degraded by various weather conditions using paired-domain token completion.

Keywords:
adverse-weather restorationconditional diffusioncontinuous visual tokensmasked autoregressive modeling

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Image restoration under adverse weather is crucial for perception and vision tasks.
  • Current methods often focus on single degradation types, limiting their applicability.
  • A unified approach for multi-weather restoration is needed.

Purpose of the Study:

  • To introduce WeatherMAR, a multi-weather restoration framework.
  • To address the limitations of single-degradation restoration methods.
  • To formulate adverse-weather restoration as a paired-domain completion problem.

Main Methods:

  • WeatherMAR uses a shared continuous token space for degraded and clean image sequences.
  • It employs masked autoregressive modeling with self-attention for cross-domain interaction.
  • Complementary bidirectional masking and a conditional diffusion objective enhance restoration.

Main Results:

  • Achieved state-of-the-art performance on Snow100K-S, Outdoor-Rain, and RainDrop benchmarks.
  • Demonstrated superior PSNR and SSIM scores across various adverse weather conditions.
  • WeatherMAR significantly outperforms existing methods in multi-weather image restoration.

Conclusions:

  • Paired-domain token completion offers an effective solution for adverse-weather image restoration.
  • WeatherMAR provides a robust and versatile framework for handling diverse weather degradations.
  • The proposed method advances the field of multi-weather image restoration.