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

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.