LLNet: A deep autoencoder approach to natural low-light image enhancement

In surveillance, monitoring and tactical reconnaissance, gathering visual information from a dynamic environment and accurately processing such data are essential to making informed decisions and ensuring the success of a mission. Camera sensors are often cost-limited to capture clear images or vide...

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Vydané v:Pattern recognition Ročník 61; s. 650 - 662
Hlavní autori: Lore, Kin Gwn, Akintayo, Adedotun, Sarkar, Soumik
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.01.2017
Predmet:
Deep autoencoders
Image enhancement
Natural low-light images
Image enhancement
Deep autoencoders
Natural low-light images
ISSN:0031-3203, 1873-5142
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Shrnutí:In surveillance, monitoring and tactical reconnaissance, gathering visual information from a dynamic environment and accurately processing such data are essential to making informed decisions and ensuring the success of a mission. Camera sensors are often cost-limited to capture clear images or videos taken in a poorly-lit environment. Many applications aim to enhance brightness, contrast and reduce noise content from the images in an on-board real-time manner. We propose a deep autoencoder-based approach to identify signal features from low-light images and adaptively brighten images without over-amplifying/saturating the lighter parts in images with a high dynamic range. We show that a variant of the stacked-sparse denoising autoencoder can learn from synthetically darkened and noise-added training examples to adaptively enhance images taken from natural low-light environment and/or are hardware-degraded. Results show significant credibility of the approach both visually and by quantitative comparison with various techniques. •Novel application of stacked sparse denoising autoencoder enhances low-light images.•Simultaneous learning of contrast-enhancement and denoising (LLNet).•Sequential learning of contrast-enhancement and denoising (Staged LLNet).•Synthetically trained model evaluated on natural low-light images.•Learned features visualized to gain insights about the model.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2016.06.008