Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 81, Issue 4, Pages 4961-4978Publisher
SPRINGER
DOI: 10.1007/s11042-021-11590-9
Keywords
Low-light image; Saliency map; Depth map; CNN; Image enhancement
Categories
Funding
- National Key R&D Program of China [2021YFB2401904]
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In this paper, a novel method is proposed for subjective low-light image enhancement by incorporating foreground saliency detection and Retinex techniques, which effectively improve the visual perception of low-light images.
Most existing low-light image enhancement methods enhance whole low-light image indiscriminately with the neglect of its subjective content, which may lead to over-enhancement and noise amplification problems in background. In this paper, we explore the challenging subjective low-light image enhancement problem. To this end, we first develop a novel foreground saliency detection model to measure the subjective content of low-light images. It is achieved by learning a saliency map and a depth map of low-light images based on CNN technique, and then fusing the two maps based on the Guided filter. Then, we incorporate the foreground saliency map model into a general retinex-based low-light image enhancement framework. Experimental results show that the proposed method well improves the subjective perception of low-light images without amplifying the noise in background compared with existing methods.
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