Low light image enhancement with adaptive sigmoid transfer function. Issue 4 (18th February 2020)

• Record Type: Journal Article
• Authors: Srinivas, Kankanala; Bhandari, Ashish Kumar
• Is Part Of: IET image processing. Volume 14:Issue 4(2020)
• Page Start: 668
• Page End: 678
• Publication Date: 2020-02-18
• Subjects: image enhancement -- computer vision -- neural nets -- transfer functions; adaptive sigmoid transfer function -- visually pleasant images -- computer vision applications -- low light images -- visual aesthetics -- high computational complexity -- visual quality -- computationally efficient low light image enhancement framework -- sigmoid activation function -- contrast‐enhanced images; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0781
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Low light image enhancement algorithms intent to produce visually pleasant images and target to extract valuable information for computer vision applications. The task of improving the quality of low light images is a challenging one. The existing methods for quality improvement undeniably annoy the visual aesthetics and suffer the major drawback of high computational complexity and less efficiency. To improve the visual quality and lower the distortions, a simple and computationally efficient low light image enhancement framework is presented in this study. To achieve this, an adaptive sigmoid transfer function (ASTF) is used and is derived from the sigmoid activation function of neural networks. By combining ASTF with a Laplacian operator, colour and contrast‐enhanced images are obtained. Experiments show the effectiveness of the proposed method with state‐of‐the‐art methods.