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arXiv · 1511.03995

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

Kin Gwn Lore·Adedotun Akintayo·Soumik Sarkar
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Abstract

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.

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Kin Gwn Lore, Adedotun Akintayo, Soumik Sarkar. 2016-04-15. LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement. https://arxiv.org/abs/1511.03995

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