Work

HDD-Unet: A Unet-based architecture for low-light image enhancement

Year: 2025

Type: article

Abstract: Low-light imaging has become a popular topic in image processing, with the quality enhancement of low light images being as a significant challenge, due to the difficulty in retaining colors, patterns, texture and style when generating a normal light image. Our objectives are mainly to firstly better preserve texture regions in image enhancement, while, secondly, preserving colors via color histogram blocks and, finally, to enhance the quality of image through dense denoising blocks. Our proposed novel framework, namely HDD-Unet, is a double Unet based on photorealistic style transfer for low-light image enhancement. The proposed low-light image enhancement method combines color histogram-based fusion, Haar wavelet pooling, dense-denoising blocks and U-net as a backbone architecture to enhance the contrast, reduce noise, and improve the visibility of low light images. Experimental results demonstrate that our proposed method outperforms existing methods in terms of PSNR and SSIM quantitative evaluation metrics, reaching or outperforming state-of-the-art accuracy, but with less resources. We also conduct an ablation study to investigate the impact of our approach on overexposed images, and systematic analysis on the late fusion weighting parameters. Multiple experiments were conducted with artificial noise inserted to accomplish more efficient comparison. The results show that the proposed framework enhances accurately images with various gamma corrections. The proposed method represents a significant advance in the field of low light image enhancement and has the potential to address several challenges associated with low light imaging. • Low-light image enhancement based on a combination of two U-Net branches with wavelet transformations, Adaptive Instance Normalization, color histogram blocks and denoising dense blocks. • Analytic framework creation for the HDD-Unet architecture. • Evaluation of the proposed method in a qualitative and a quantitative way.

Source: Image and Vision Computing

DOI: 10.1016/j.imavis.2025.105889

Authors: Elissavet Batziou, Konstantinos Ioannidis, Ioannis Patras, Stefanos Vrochidis, Ioannis Kompatsiaris

Institutions: Queen Mary University of London, Information Technologies Institute, Centre for Research and Technology Hellas

Language: English

Topic: Image Enhancement Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,100

Funder: European Commission