LLNet: A deep autoencoder approach to natural low-light image enhancement
作者：
Highlights：
• 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.
摘要
Highlights•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.
论文关键词：Image enhancement,Natural low-light images,Deep autoencoders
论文评审过程：Received 29 January 2016, Revised 10 June 2016, Accepted 11 June 2016, Available online 15 June 2016, Version of Record 13 October 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.06.008
