A novel visual perception enhancement algorithm for high-speed railway in the low light condition

Lyu, Guohao; Huang, Hua; Yin, Hui; Luo, Siwei; Jiang, Xinlan

doi:10.1109/icosp.2014.7015159

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…(∂f /∂x) 2 + (∂f /∂y) 2 2 (32) where M and N are the image width and height, respectively, and ∂f /∂x and ∂f /∂x are the horizontal and vertical gradients, respectively. (iv) Information entropy (IE).…”

Section: ) No-reference Iqa (Niqa) Metricsmentioning

confidence: 99%

“…Related research results can be widely applied in fields such as urban traffic monitoring, outdoor video acquisition, satellite remote sensing, and military aviation investigation and can be used as a reference for studies on topics such as underwater image analysis and haze image clarity [31]. Moreover, as an important branch of research in the field of image processing, low-light image enhancement has interdisciplinary and innovative appeal and broad application prospects and has become a focus of interdisciplinary research in recent years [32]. A large number of researchers at home and abroad have been paying increasing attention to this field for quite some time [33]- [38].…”

Section: Introductionmentioning

confidence: 99%

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An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested. INDEX TERMS Review, survey, low-light image enhancement, Retinex method, image enhancement, quality evaluation.

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Section: ) No-reference Iqa (Niqa) Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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“…Image enhancement has a wide range of applications in different fields, e.g., underwater images [ 6 ], high-speed railway images [ 7 ], and robot vision [ 8 ]. In general, there are two ways to improve the image quality.…”

Section: Introductionmentioning

confidence: 99%

Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior

Gao

Zhang

Luo

2022

Sensors

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Low-light images are a common phenomenon when taking photos in low-light environments with inappropriate camera equipment, leading to shortcomings such as low contrast, color distortion, uneven brightness, and high loss of detail. These shortcomings are not only subjectively annoying but also affect the performance of many computer vision systems. Enhanced low-light images can be better applied to image recognition, object detection and image segmentation. This paper proposes a novel RetinexDIP method to enhance images. Noise is considered as a factor in image decomposition using deep learning generative strategies. The involvement of noise makes the image more real, weakens the coupling relationship between the three components, avoids overfitting, and improves generalization. Extensive experiments demonstrate that our method outperforms existing methods qualitatively and quantitatively.

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