  In surveillance, monitoring and tactical reconnaissance, gathering the right
visual information from a dynamic environment and accurately processing such
data are essential ingredients to making informed decisions which determines
the success of an operation. Camera sensors are often cost-limited in ability
to clearly capture objects without defects from images or videos taken in a
poorly-lit environment. The goal in many applications is to enhance the
brightness, contrast and reduce noise content of such images in an on-board
real-time manner. We propose a deep autoencoder-based approach to identify
signal features from low-light images handcrafting and adaptively brighten
images without over-amplifying the lighter parts in images (i.e., without
saturation of image pixels) in high dynamic range. We show that a variant of
the recently proposed stacked-sparse denoising autoencoder can learn to
adaptively enhance and denoise from synthetically darkened and noisy training
examples. The network can then be successfully applied to naturally low-light
environment and/or hardware degraded images. Results show significant
credibility of deep learning based approaches both visually and by quantitative
comparison with various popular enhancing, state-of-the-art denoising and
hybrid enhancing-denoising techniques.
