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In surveillance, monitoring and tactical reconnaissance, gathering the rightvisual information from a dynamic environment and accurately processing suchdata are essential ingredients to making informed decisions which determinesthe success of an operation. Camera sensors are often cost-limited in abilityto clearly capture objects without defects from images or videos taken in apoorly-lit environment. The goal in many applications is to enhance thebrightness, contrast and reduce noise content of such images in an on-boardreal-time manner. We propose a deep autoencoder-based approach to identifysignal features from low-light images handcrafting and adaptively brightenimages without over-amplifying the lighter parts in images (i.e., withoutsaturation of image pixels) in high dynamic range. We show that a variant ofthe recently proposed stacked-sparse denoising autoencoder can learn toadaptively enhance and denoise from synthetically darkened and noisy trainingexamples. The network can then be successfully applied to naturally low-lightenvironment and/or hardware degraded images. Results show significantcredibility of deep learning based approaches both visually and by quantitativecomparison with various popular enhancing, state-of-the-art denoising andhybrid enhancing-denoising techniques.

LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement