Natural-appearance Colorization and Enhancement for the Low-light-level Night Vision Imaging

Jin, 朱进 Zhu; Li, 李力 Li; Weiqi, 金伟其 Jin; Shuo, 李硕 Li; Xia, 王霞 Wang; Xiao-feng, 拜晓峰 Bai

doi:10.3788/gzxb20184704.0410002

Cited by 1 publication

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“…At present, in order to weaken the influence of low-light images on visual tasks, researchers have proposed two solutions. One is to improve the performance of image acquisition equipment [7,8], and the other is to perform illumination enhancement processing on the collected low-light images. Existing low-light cameras from companies such as Sony, Photonis, SiOnyx, and Texas Instruments use high-performance charge-coupled devices or complementary met-al-oxide-semiconductor [9] technology, professional low-light circuits, and filters as core components to improve low-light image quality.…”

Section: Introductionmentioning

confidence: 99%

MTIE-Net: Multi-technology fusion of low-light image enhancement network

Tao,

Wu,

et al. 2024

PLoS ONE

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Images obtained in low-light scenes are often accompanied by problems such as low visibility, blurred details, and color distortion, enhancing them can effectively improve the visual effect and provide favorable conditions for advanced visual tasks. In this study, we propose a Multi-Technology Fusion of Low-light Image Enhancement Network (MTIE-Net) that modularizes the enhancement task. MTIE-Net consists of a residual dense decomposition network (RDD-Net) based on Retinex theory, an encoder-decoder denoising network (EDD-Net), and a parallel mixed attention-based self-calibrated illumination enhancement network (PCE-Net). The low-light image is first decomposed by RDD-Net into a lighting map and reflectance map; EDD-Net is used to process noise in the reflectance map; Finally, the lighting map is fused with the denoised reflectance map as an input to PCE-Net, using the Fourier transform for illumination enhancement and detail recovery in the frequency domain. Numerous experimental results show that MTIE-Net outperforms the comparison methods in terms of image visual quality enhancement improvement, denoising, and detail recovery. The application in nighttime face detection also fully demonstrates its promise as a pre-processing means in practical applications.

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Section: Introductionmentioning

confidence: 99%