A Convergent Neural Network for Non-Blind Image Deblurring

Yanan Zhao, Yuelong Li, Haichuan Zhang, Vishal Monga, Yonina C. Eldar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


In recent years, algorithm unrolling has emerged as a powerful technique for designing interpretable neural networks based on iterative algorithms. Imaging inverse problems have particularly benefited from unrolling based deep network design since many traditional model-based approaches rely on iterative optimization. Despite exciting progress, typical unrolling approaches heuristically design layer-specific convolution weights to improve performance. Crucially, convergence properties of the underlying iterative algorithm are lost once layer specific parameters are learned from training data. In this paper, we propose a neural network architecture that breaks the trade-off between retaining algorithm properties while simultaneously enhancing performance. We focus on non-blind image deblurring problem and unroll the widely-applied Half-Quadratic Splitting (HQS) algorithm. We develop a new parameterization scheme that enforces the layer-specific parameters to asymptotically approach certain fixed points, a new result that we analytically establish. Experimental results show that our approach outperforms many state of the art non-blind deblurring techniques on benchmark datasets, while enabling convergence and interpretability.

Original languageEnglish (US)
Title of host publication2023 IEEE International Conference on Image Processing, ICIP 2023 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781728198354
StatePublished - 2023
Event30th IEEE International Conference on Image Processing, ICIP 2023 - Kuala Lumpur, Malaysia
Duration: Oct 8 2023Oct 11 2023

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference30th IEEE International Conference on Image Processing, ICIP 2023
CityKuala Lumpur

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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