LLNet: A deep autoencoder approach to natural low-light image enhancement
TL
TL;DR
It is shown 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.
References52 items
1
Deep Value of Information Estimators for Collaborative Human-Machine Information Gathering
2
Early Detection of Combustion Instability from Hi-speed Flame Images via Deep Learning and Symbolic Time Series Analysis
3
Low Light Image Enhancement via Sparse Representations
4
Learning to Deblur
5