Exploring Image Enhancement for Salient Object Detection in Low Light Images

• URL: http://arxiv.org/abs/2007.16124v1
• Date: Fri, 31 Jul 2020 15:09:03 GMT
• Title: Exploring Image Enhancement for Salient Object Detection in Low Light Images
• Authors: Xin Xu, Shiqin Wang, Zheng Wang, Xiaolong Zhang, and Ruimin Hu
• Abstract summary: We propose an image enhancement approach to facilitate the salient object detection in low light images. The proposed model embeds the physical lighting model into the deep neural network to describe the degradation of low light images. We construct a low light Images dataset with pixel-level human-labeled ground-truth annotations and report promising results.
• Score: 27.61080096436953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low light images captured in a non-uniform illumination environment usually are degraded with the scene depth and the corresponding environment lights. This degradation results in severe object information loss in the degraded image modality, which makes the salient object detection more challenging due to low contrast property and artificial light influence. However, existing salient object detection models are developed based on the assumption that the images are captured under a sufficient brightness environment, which is impractical in real-world scenarios. In this work, we propose an image enhancement approach to facilitate the salient object detection in low light images. The proposed model directly embeds the physical lighting model into the deep neural network to describe the degradation of low light images, in which the environment light is treated as a point-wise variate and changes with local content. Moreover, a Non-Local-Block Layer is utilized to capture the difference of local content of an object against its local neighborhood favoring regions. To quantitative evaluation, we construct a low light Images dataset with pixel-level human-labeled ground-truth annotations and report promising results on four public datasets and our benchmark dataset.

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