LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
This work addresses the challenge of improving image quality in poorly-lit environments for applications like surveillance and monitoring, representing an incremental advancement in deep learning-based enhancement methods.
The authors tackled the problem of enhancing low-light images by proposing a deep autoencoder approach that adaptively brightens and denoises images without saturating lighter parts, achieving results that are both visually credible and quantitatively competitive with state-of-the-art techniques.
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.