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High-resolution image de-raining using conditional GAN with sub-pixel upscaling

Abstract

High-quality image de-raining is a challenging task that has been given considerable importance in recent times. To begin with, this problem is modeled as an image decomposition task where a rainy image is decomposed into the rain-free background and the associated rain streak map. Most of the existing methods have been successful in removing the rain-streaks but fails to restore the image quality, which is degraded due to noise removal. This paper proposes a novel architecture called High-Resolution Image De-Raining using Conditional Generative Adversarial Networks (HRID-GAN) to generate a de-rained image with minimal artifacts and better visual quality. Extensive experiments on publicly available synthetic as well as real-world datasets show a substantial improvement over the state-of-the-art methods SPANet (Wang et al. 2019) by ∼ 2.43% in PSNR and, DID-MDN (Zhang and Patel 2018) by ∼ 2.43%, ∼ 10.12% and ID-CGAN (Zhang et al. 2017) by ∼ 11.80%, ∼ 34.70% in SSIM and PSNR respectively.

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Notes

  1. Transpose convolution is also referred as Deconvolution

  2. https://github.com/hezhangsprinter/DIDMDN

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Acknowledgments

Authors would like to thank the anonymous reviewers for their insightful comments and suggestions. Authors would also like to acknowledge the funding agency, Ministry of Human Resource Development, Government of India.

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Correspondence to Prasen Kumar Sharma.

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Sharma, P.K., Basavaraju, S. & Sur, A. High-resolution image de-raining using conditional GAN with sub-pixel upscaling. Multimed Tools Appl 80, 1075–1094 (2021). https://doi.org/10.1007/s11042-020-09642-7

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  • DOI: https://doi.org/10.1007/s11042-020-09642-7

Keywords

  • Image restoration
  • Deep learning
  • Conditional GAN