Low-light image analysis and contrast enhancement using gaussian process / Loh Yuen Peng - Loh , Yuen Peng (2018)
Low-light is an inescapable element in daily surroundings that greatly affects the efficiency of human vision. However, current studies in low-light fundamentally lack an indepth understanding of natural vision in low-light that would strengthen the development of effective algorithms. This has subsequently restricted the development of well-rounded systems that would aid in low-light environments, such as assistive systems, surveillance, and autonomous car driving. Therefore, this thesis aims to study low-light image data to gain a better understanding of their characteristics, and then based on this understanding, investigate a computer vision solution that would pave the way for the advancement of future assistive systems to operate in low-light conditions. An obvious challenge faced in this study is the lack of a go-to database in this domain, hence led to the first contribution that is a collection of 7,363 low-light images gathered from multiple sources, with 12 object classes annotation in order to facilitate the analysis for the purpose of applications. From this dataset, it was found that low-light environments can be categorized into 10 illumination types, each with different global and local characteristics that could have different impact on a system. The second contribution is an in-depth analysis of the collected data, specifically, by studying the global and local pixel intensities, followed by the performance and visualizations of hand-crafted and learned features. It is found that characteristics of the low-light pixel intensities provide a great challenge to algorithms. The design of conventional hand-crafted features are greatly rooted to the behaviors of bright environments, that they are unable to adequately address noise and lack of details accompanying low-light images. Whereas, learned features revealed that the same object yields amply different features in bright and low-light conditions, and irregular illumi nation greatly challenges the attention of the said features. These insights prompt the third contribution, to propose a low-light contrast enhancement algorithm that is not only able to improve the visibility but more importantly to reveal informative features to assist high level applications. To this end, the Gaussian Process is studied as the contrast enhancement approach to model the complexity of the local luminance variations, the primary difficulty in low-light images. Experimental results show that the proposed method outperforms the state-of-the-art in the common visual quality measure, the peak signalto- noise ratio (PSNR) by 1.17dB. Additionally, novel information retrieval measurements are proposed to better evaluate the usefulness of enhancement algorithms in applications, namely the local features matching and l1-norm distance measure of intensity histogram. Both of which the proposed method outperforms the state-of-the-art method by a large margin, signifying the applicability of the proposal to support computer vision systems. As a whole, the contributions of this study will push forward the advancement of computer vision towards practicality in low-light environments which will be particularly valuable in the development of assistive and surveillance systems that ensure the quality of life and safety of the public