High dynamic range

Contrast (vision)

Computer science

Brightness

Geology

Paleontology

Physics

Pattern recognition (psychology)

Optics

Image (mathematics)

Autoencoder

Natural (archaeology)

Artificial intelligence

Deep learning

Image enhancement

Noise reduction

Noise (video)

Dynamic range

Computer vision

Pattern Recognition

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.

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