Low light image enhancement with adaptive sigmoid transfer function
| Autor: | Ashish Kumar Bhandari, Kankanala Srinivas |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computational complexity theory
Artificial neural network business.industry Computer science 020206 networking & telecommunications 02 engineering and technology Image enhancement Transfer function Quality (physics) Sigmoid activation function Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering business Laplace operator Software Sigmoid transfer function |
| Zdroj: | IET Image Processing. 14:668-678 |
| ISSN: | 1751-9667 |
| DOI: | 10.1049/iet-ipr.2019.0781 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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