This paper proposes a deep autoencoder-based approach to identify signal features from lowlight images and adaptively brighten images without over-amplifying/saturating the lighter parts in images with a high dynamic range. 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 show that a variant of the stacked-sparse denoising autoencoder can learn to adaptively enhance and denoise from synthetically darkened and noise-added training examples. The model can be applied to images taken from natural lowlight environment and/or are hardware-degraded. Results show significant credibility of the approach both visually and by quantitative comparison with various image enhancement techniques.
A recent neural network based attempt is made to identify signal features from lowlight images and brighten images adaptively without overamplifying or saturating the lighter parts in images with a high dynamic range REF .
LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
ArXiv
ArXiv
Computer Science Mathematics