A Biological Vision Inspired Framework for Image Enhancement in Poor Visibility Conditions

• Published in: IEEE transactions on image processing Vol. 29; pp. 1493 - 1506
• Main Authors: Yang, Kai-Fu, Zhang, Xian-Shi, Li, Yong-Jie
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Computer vision; Datasets; Dynamic range; HDR image; Image enhancement; low-light image; Luminance; Mapping; Nighttime image; Noise reduction; noise suppression; Task analysis; Visibility; visual adaptation; Visual systems; Visual tasks; Visualization
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2019.2938310

Image enhancement is an important pre-processing step for many computer vision applications especially regarding the scenes in poor visibility conditions. In this work, we develop a unified two-pathway model inspired by the biological vision, especially the early visual mechanisms, which contributes to image enhancement tasks including low dynamic range (LDR) image enhancement and high dynamic range (HDR) image tone mapping. Firstly, the input image is separated and sent into two visual pathways: structure-pathway and detail-pathway, corresponding to the M- and P-pathway in the early visual system, which code the low- and high-frequency visual information, respectively. In the structure-pathway, an extended biological normalization model is used to integrate the global and local luminance adaptation, which can handle the visual scenes with varying illuminations. On the other hand, the detail enhancement and local noise suppression are achieved in the detail-pathway based on local energy weighting. Finally, the outputs of structure-and detail-pathway are integrated to achieve the low-light image enhancement. In addition, the proposed model can also be used for tone mapping of HDR images with some fine-tuning steps. Extensive experiments on three datasets (two LDR image datasets and one HDR scene dataset) show that the proposed model can handle the visual enhancement tasks mentioned above efficiently and outperform the related state-of-the-art methods.