Fast color contrast enhancement method for color night vision

Qian, Xiaoyan; Wang, Yujin; Wang, Bangfeng

doi:10.1016/j.infrared.2011.10.008

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…The method in [28] enhanced the contrast in nighttime images by using the concept of lab color space. Then, the model in [29] enhanced images based on histogram and fuzzy-logic.…”

Section: Literature Reviewmentioning

confidence: 99%

A Smart System for Low-Light Image Enhancement with Color Constancy and Detail Manipulation in Complex Light Environments

Rahman

Aamir

et al. 2018

Symmetry

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Images are an important medium to represent meaningful information. It may be difficult for computer vision techniques and humans to extract valuable information from images with low illumination. Currently, the enhancement of low-quality images is a challenging task in the domain of image processing and computer graphics. Although there are many algorithms for image enhancement, the existing techniques often produce defective results with respect to the portions of the image with intense or normal illumination, and such techniques also inevitably degrade certain visual artifacts of the image. The model use for image enhancement must perform the following tasks: preserving details, improving contrast, color correction, and noise suppression. In this paper, we have proposed a framework based on a camera response and weighted least squares strategies. First, the image exposure is adjusted using brightness transformation to obtain the correct model for the camera response, and an illumination estimation approach is used to extract a ratio map. Then, the proposed model adjusts every pixel according to the calculated exposure map and Retinex theory. Additionally, a dehazing algorithm is used to remove haze and improve the contrast of the image. The color constancy parameters set the true color for images of low to average quality. Finally, a details enhancement approach preserves the naturalness and extracts more details to enhance the visual quality of the image. The experimental evidence and a comparison with several, recent state-of-the-art algorithms demonstrated that our designed framework is effective and can efficiently enhance low-light images.

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“…The method in [28] enhanced the contrast in nighttime images by using the concept of lab color space. Then, the model in [29] enhanced images based on histogram and fuzzy-logic.…”

Section: Literature Reviewmentioning

confidence: 99%

A Smart System for Low-Light Image Enhancement with Color Constancy and Detail Manipulation in Complex Light Environments

Rahman

Aamir

et al. 2018

Symmetry

View full text Add to dashboard Cite

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“…Image fusion is also used in intelligent transport [8,9], where feature-based fusion, reliant on identifying stable characteristics that are present in multiple images, aids in the automated recognition of cars and other road vehicles. The previously described techniques are also employed in surveillance [10], low altitude remote sensing [11], and color visibility enhancement [12], among others.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Enhanced and Synthetic Vision System Images for Runway and Horizon Detection

Fadhil

Kanneganti

Gupta

et al. 2019

Sensors

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Networked operation of unmanned air vehicles (UAVs) demands fusion of information from disparate sources for accurate flight control. In this investigation, a novel sensor fusion architecture for detecting aircraft runway and horizons as well as enhancing the awareness of surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented in real-world situations due to signal misalignment. We address this through a registration step to align EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented, and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. The fusion architecture developed in this study holds promise for incorporation into manned heads-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provides a basis for rule selection in other signal fusion applications.

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“…It has a wide range of applications in remote sensing, intelligent surveillance, medical imaging, astrophysics, etc. [1]- [3]. Image fusion is performed on pixels, features, and decision levels [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Entropy dependent compressive sensing based image fusion

Jameel

Ghafoor

Riaz

2013

2013 International Symposium on Intelligent Signal Processing and Communication Systems

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An entropy dependent compressive sensing based image fusion is proposed which adaptively adjusts the number of compressive measurements depending on the amount of information (computed using entropy). Since the computational complexity is dependent on the number of compressive measurements, the proposed scheme is more efficient and accurate. Simulation results show the significance of the proposed scheme compared to existing compressive sensing based state of art scheme.

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Fast color contrast enhancement method for color night vision

Cited by 12 publications

References 21 publications

A Smart System for Low-Light Image Enhancement with Color Constancy and Detail Manipulation in Complex Light Environments

A Smart System for Low-Light Image Enhancement with Color Constancy and Detail Manipulation in Complex Light Environments

Fusion of Enhanced and Synthetic Vision System Images for Runway and Horizon Detection

Entropy dependent compressive sensing based image fusion

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