Single low-light image brightening using learning-based intensity mapping

Wang, Xiaocheng; Hu, Ruimin; Xu, Xin

doi:10.1016/j.neucom.2022.08.042

Cited by 5 publications

(1 citation statement)

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“…Inspired by image-to-curve transform and multi-exposure fusion, Wang et al [25] proposed a new method to treat the low-light image enhancement task as an extended problem with multiple virtual exposures, using nonlinear intensity mapping. Considering the difficulty for existing image-tocurve methods to obtain the desired detail and recover the expected brightness in any one iteration without relying on any ground truth, a virtual multi-exposure fusion strategy was proposed to merge the outputs of these different iterations.…”

Section: Introductionmentioning

confidence: 99%

Low-Light Image Enhancement and Target Detection Based on Deep Learning

Yao¹

2022

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Most computer vision applications demand input images to meet their specific requirements. To complete different vision tasks, e.g., object detection, object recognition, and object retrieval, low-light images must be enhanced by different methods to achieve different processing effects. The existing image enhancement methods, which are based on non-physical imaging models, and image generation methods, which are based on deep learning, are not ideal for low-light image processing. To solve the problem, this paper explores low-light image enhancement and target detection based on deep learning. Firstly, a simplified expression was constructed for the optical imaging model of low-light images, and a Haze-line was proposed for color correction of low-light images, which can effectively enhance low-light images based on the global background light and medium transmission rate of the optical imaging model of such images. Next, network framework adopted by the proposed low-light image enhancement model was introduced in detail: the framework includes two deep domain adaptation modules that realize domain transformation and image enhancement, respectively, and the loss functions of the model were presented. To detect targets based on the output enhanced image, a joint enhancement and target detection method was proposed for low-light images. The effectiveness of the constructed model was demonstrated through experiments.

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Section: Introductionmentioning

confidence: 99%