DarkDeblur: Learning single-shot image deblurring in low-light condition
- URL: http://arxiv.org/abs/2503.02194v1
- Date: Tue, 04 Mar 2025 02:04:50 GMT
- Title: DarkDeblur: Learning single-shot image deblurring in low-light condition
- Authors: S M A Sharif, Rizwan Ali Naqvi, Farman Alic, Mithun Biswas,
- Abstract summary: This study tackles the limitations of the low-light image deblurring with a learning-based approach and proposes a novel deep network named as DarkDeNet.<n>The proposed DarkDe- Net comprises a dense-attention block and a contextual gating mechanism in a feature pyramid structure to leverage content awareness.<n>The practicability of the proposed model has been verified by fusing it in numerous computer vision applications.
- Score: 10.806039226682143
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Single-shot image deblurring in a low-light condition is known to be a profoundly challenging image translation task. This study tackles the limitations of the low-light image deblurring with a learning-based approach and proposes a novel deep network named as DarkDeblurNet. The proposed DarkDeblur- Net comprises a dense-attention block and a contextual gating mechanism in a feature pyramid structure to leverage content awareness. The model additionally incorporates a multi-term objective function to perceive a plausible perceptual image quality while performing image deblurring in the low-light settings. The practicability of the proposed model has been verified by fusing it in numerous computer vision applications. Apart from that, this study introduces a benchmark dataset collected with actual hardware to assess the low-light image deblurring methods in a real-world setup. The experimental results illustrate that the proposed method can outperform the state-of-the-art methods in both synthesized and real-world data for single-shot image deblurring, even in challenging lighting environment.
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