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Blind Image Deblurring with Noise-Robust Kernel Estimation

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15078))

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Abstract

Blind deblurring is an ill-posed inverse problem involving the retrieval of a clear image and blur kernel from a single blurry image. The challenge arises considerably when strong noise, where its level remains unknown, is introduced. Existing blind deblurring methods are highly susceptible to noise due to overfitting and disturbances in the solution space. Here, we propose a blind deblurring method based on a noise-robust kernel estimation function and deep image prior (DIP). Specifically, the proposed kernel estimation function effectively estimates the blur kernel even for strongly noisy blurry images given a clear image and optimal condition. Therefore, DIP is adopted for the generation of a clear image to leverage its natural image prior. Additionally, the multiple kernel estimation scheme is designed to address a wide range of unknown noise levels. Extensive experimental studies, including simulated images and real-world examples, demonstrate the superior deblurring performance of the proposed method. The official code is uploaded in https://github.com/csleemooo/BD_noise_robust_kernel_estimation.

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Acknowledgements

This work was supported by the Samsung Research Funding and Incubation Center of Samsung Electronics grant SRFC-IT2002-03, Samsung Electronics Co., Ltd. (IO220908-02403-01), and the National Research Foundation of Korea grant funded by the Korean government (Grant Nos. NRF-2021R1A5A1032937, NRF-2021R1C1C1011307, RS-2023-00251628, and RS-2024-00397673).

Author information

Authors and Affiliations

  1. Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea

    Chanseok Lee, Jeongsol Kim, Seungmin Lee & Mooseok Jang

  2. Advanced Process Development Team 4, Semiconductor R&D Center, Samsung Electronics, Suwon, South Korea

    Jaehwang Jung, Yunje Cho, Taejoong Kim, Taeyong Jo & Myungjun Lee

  3. KAIST Institute for Health Science and Technology, KAIST, Daejeon, South Korea

    Mooseok Jang

Authors
  1. Chanseok Lee
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  3. Seungmin Lee
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Corresponding authors

Correspondence to Chanseok Lee or Mooseok Jang .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Lee, C. et al. (2025). Blind Image Deblurring with Noise-Robust Kernel Estimation. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15078. Springer, Cham. https://doi.org/10.1007/978-3-031-72661-3_11

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  • DOI: https://doi.org/10.1007/978-3-031-72661-3_11

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