LLNet: A deep autoencoder approach to natural low-light image enhancement
@article{Lore2015LLNetAD, title={LLNet: A deep autoencoder approach to natural low-light image enhancement}, author={Kin Gwn Lore and Adedotun Akintayo and Soumik Sarkar}, journal={Pattern Recognition}, year={2015}, volume={61}, pages={650-662} }
Abstract 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… CONTINUE READING
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References
Publications referenced by this paper.
SHOWING 1-10 OF 20 REFERENCES
Image restoration by sparse 3D transform-domain collaborative filtering
VIEW 12 EXCERPTS
HIGHLY INFLUENTIAL
Indoor Semantic Segmentation using depth information
VIEW 2 EXCERPTS
Theano: new features and speed improvements
VIEW 1 EXCERPT