人工智能
计算机科学
计算机视觉
自编码
亮度
噪音(视频)
深度学习
高动态范围
降噪
对比度(视觉)
图像(数学)
动态范围
光学
物理
作者
Kin Gwn Lore,Adedotun Akintayo,Soumik Sarkar
标识
DOI:10.1016/j.patcog.2016.06.008
摘要
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 propose a deep autoencoder-based approach to identify signal features from low-light images and adaptively brighten images without over-amplifying/saturating the lighter parts in images with a high dynamic range. We show that a variant of the stacked-sparse denoising autoencoder can learn from synthetically darkened and noise-added training examples to adaptively enhance images taken from natural low-light environment and/or are hardware-degraded. Results show significant credibility of the approach both visually and by quantitative comparison with various techniques.
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