Illuminating the Roads: Night-to-Day Image Translation for Improved Visibility at Night

Lakmal, H. K. I. S.; Dissanayake, M. B.

doi:10.1007/978-3-031-51135-6_2

Communications in Computer and Information Science

2024

DOI: 10.1007/978-3-031-51135-6_2

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Illuminating the Roads: Night-to-Day Image Translation for Improved Visibility at Night

H. K. I. S. Lakmal,

M. B. Dissanayake

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A Novel Self-Adaptive Deformable Convolution-Based U-Net for Low-Light Image Denoising

Wang,

Cao,

Guo

et al. 2024

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Capturing images under extremely low-light conditions usually suffers from various types of noise due to the limited photon and low signal-to-noise ratio (SNR), which makes low-light denoising a challenging task in the field of imaging technology. Nevertheless, existing methods primarily focus on investigating the precise modeling of real noise distributions while neglecting improvements in the noise modeling capabilities of learning models. To address this situation, a novel self-adaptive deformable-convolution-based U-Net (SD-UNet) model is proposed in this paper. Firstly, deformable convolution is employed to tackle noise patterns with different geometries, thus extracting more reliable noise representations. After that, a self-adaptive learning block is proposed to enable the network to automatically select appropriate learning branches for noise with different scales. Finally, a novel structural loss function is leveraged to evaluate the difference between denoised and clean images. The experimental results on multiple public datasets validate the effectiveness of the proposed method.

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A Novel Self-Adaptive Deformable Convolution-Based U-Net for Low-Light Image Denoising

Wang,

Cao,

Guo

et al. 2024

Symmetry

View full text Add to dashboard Cite

show abstract

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Illuminating the Roads: Night-to-Day Image Translation for Improved Visibility at Night

Cited by 1 publication

References 37 publications

A Novel Self-Adaptive Deformable Convolution-Based U-Net for Low-Light Image Denoising

A Novel Self-Adaptive Deformable Convolution-Based U-Net for Low-Light Image Denoising

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