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A dark image enhancement method based on multiscale features and dilated residual networks

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Abstract

In low-light scenes, due to the limitations of ambient light and camera hardware equipment, the images captured by imaging devices often have low brightness, low contrast, high noise and loss of detail, which can cause great interference in face recognition, video surveillance and other application scenarios. Accordingly, a dark image enhancement method based on multi-scale features and dilated residual networks is proposed to solve the above problem. The input to the network is the V channel of the image HSV color space. The network uses a multiscale feature extractor to extract shallow features from the image, then a new dilated residual network constructed in this paper is used to extract deep features from the image, and finally the enhanced V-component is obtained by a single-channel convolutional layer. The final enhanced low-illumination image is obtained by component fusion in this paper. The experimental results show that compared with the existing mainstream algorithms, the algorithm in this paper has good subjective evaluation, natural image enhancement, no distortion in color, and high network robustness. In terms of objective metrics, the PSNR, SSIM, MSE, image mean value and image information entropy of the algorithms in this paper are significantly improved over other algorithms. Among them, in the dataset LOL with reference images, PSNR, SSIM and MSE are improved by about 61.55, 9.42 and 861.7% respectively. In the datasets Exdark and DARK FACE without reference images, the image mean and image information entropy are improved by about 20.728 and 2.31% respectively.

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Data Availability Statement

LOL dataset: BMVC2018 Deep Retinex Decomposition (daooshee.github.io) Exdark dataset: GitHub—cs-chan/Exclusively-Dark-Image-Dataset: Exclusively Dark (ExDARK) dataset which to the best of our knowledge, is the largest collection of low-light images taken in very low-light environments to twilight (i.e. 10 different conditions) to-date with image class and object level annotations. DARK FACE dataset: DARK FACE: Face Detection in Low Light Condition (flyywh.github.io).

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Funding

This work was supported by the science and Technology Department of Jilin Province, China (No. 20180623039TC).

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Authors and Affiliations

  1. Institute of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China

    Xingrui Wang, Yan Piao & Yumo Wang

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  1. Xingrui Wang
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  2. Yan Piao
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  3. Yumo Wang
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Correspondence to Yan Piao.

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Wang, X., Piao, Y. & Wang, Y. A dark image enhancement method based on multiscale features and dilated residual networks. Neural Process Lett 54, 5525–5543 (2022). https://doi.org/10.1007/s11063-022-10872-z

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