Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement

Due to the absence of a desirable objective for low-light image enhancement, previous data-driven methods may provide undesirable enhanced results including amplified noise, degraded contrast and biased colors. In this work, inspired by Retinex theory, we design an end-to-end signal prior-guided lay...

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Vydané v:	IEEE transactions on image processing Ročník 30; s. 1
Hlavní autori:	Yang, Wenhan, Wang, Wenjing, Huang, Haofeng, Wanga, Shiqi, Liu, Jiaying
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Atmospheric modeling denoising Illumination Image coding Image compression Image enhancement Image restoration Light Lighting Low-light enhancement Minimization Noise Noise reduction Reflectance Regularization residual dense network Retinex model sparse gradient regularization
ISSN:	1057-7149, 1941-0042, 1941-0042
On-line prístup:	Získať plný text
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