Multispectral image enhancement based on illuminance-reflection imaging model and morphology operation

Wang, Dianwei; Han, Pengfei; Fan, Jiulun; Liu, Ying; Xu, Zhijie; Wang, Jing

doi:10.7498/aps.67.20181288

Cited by 9 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After the image brightness is improved, the saturation of the image will be reduced to a certain extent. In order to prevent the impact of brightness on saturation, we quote the adaptive non-linear stretching function proposed by the literature [ 24 ] to stretch the saturation of the image. After using this function to process S components, the saturation of the image is higher and the color information of the image is richer.…”

Section: The Proposed Night Image Enhancement Methodsmentioning

confidence: 99%

Nighttime Image Stitching Method Based on Image Decomposition Enhancement

Yan,

Qin,

Zhang

et al. 2023

Entropy

View full text Add to dashboard Cite

Image stitching technology realizes alignment and fusion of a series of images with common pixel areas taken from different viewpoints of the same scene to produce a wide field of view panoramic image with natural structure. The night environment is one of the important scenes of human life, and the night image stitching technology has more urgent practical significance in the fields of security monitoring and intelligent driving at night. Due to the influence of artificial light sources at night, the brightness of the image is unevenly distributed and there are a large number of dark light areas, but often these dark light areas have rich structural information. The structural features hidden in the darkness are difficult to extract, resulting in ghosting and misalignment when stitching, which makes it difficult to meet the practical application requirements. Therefore, a nighttime image stitching method based on image decomposition enhancement is proposed to address the problem of insufficient line feature extraction in the stitching process of nighttime images. The proposed algorithm performs luminance enhancement on the structural layer, smoothes the nighttime image noise using a denoising algorithm on the texture layer, and finally complements the texture of the fused image by an edge enhancement algorithm. The experimental results show that the proposed algorithm improves the image quality in terms of information entropy, contrast, and noise suppression compared with other algorithms. Moreover, the proposed algorithm extracts the most line features from the processed nighttime images, which is more helpful for the stitching of nighttime images.

show abstract

Section: The Proposed Night Image Enhancement Methodsmentioning

confidence: 99%

Nighttime Image Stitching Method Based on Image Decomposition Enhancement

Yan,

Qin,

Zhang

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…Wang et al. [29] designed the enhancement for multispectral images based on the illuminance‐reflectance imaging model, and Yu et al. [30] designed the image enhancement to epically focus on noise based on the illuminance‐reflectance imaging model.…”

Section: Related Workmentioning

confidence: 99%

“…The enhancement carried out by the illuminance-reflectance imaging model shows great potential for different applications [29][30][31][32]. Wang et al [29] designed the enhancement for multispectral images based on the illuminance-reflectance imaging model, and Yu et al [30] designed the image enhancement to epically focus on noise based on the illuminance-reflectance imaging model. Eilertsen et al [31] designed the loss function of deep learning based on the illuminance-reflectance imaging model.…”

Section: Perception Stability Of Stereo Visionmentioning

confidence: 99%

See 1 more Smart Citation

Hawk‐eye‐inspired perception algorithm of stereo vision for obtaining orchard 3D point cloud navigation map

Zhang

Chen

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

The binocular stereo vision is the lowest cost sensor for obtaining 3D information. Considering the weakness of long-distance measurement and stability, the improvement of accuracy and stability of stereo vision is urgently required for application of precision agriculture. To address the challenges of stereo vision long-distance measurement and stable perception without hardware upgrade, inspired by hawk eyes, higher resolution perception and the adaptive HDR (High Dynamic Range) were introduced in this paper. Simulating the function from physiological structure of 'deep fovea' and 'shallow fovea' of hawk eye, the higher resolution reconstruction method in this paper was aimed at accuracy improving. Inspired by adjustment of pupils, the adaptive HDR method was proposed for high dynamic range optimisation and stable perception. In various light conditions, compared with default stereo vision, the accuracy of proposed algorithm was improved by 28.0% evaluated by error ratio, and the stability was improved by 26.56% by disparity accuracy. For fixed distance measurement, the maximum improvement was 78.6% by standard deviation. Based on the hawk-eye-inspired perception algorithm, the point cloud of orchard was improved both in quality and quantity. The hawk-eye-inspired perception algorithm contributed great advance in binocular 3D point cloud reconstruction in orchard navigation map. K E Y W O R D S adaptive high dynamic range, binocular stereo vision, hawk-eye-inspired perception, point cloud of orchard, super-resolution generative adversarial network 1 | INTRODUCTION Precision agriculture needs accurate perception. In orchard automation agricultural production, accurate and stable 3D navigation map is the basis of UAV and unmanned vehicle operation [1]. Agricultural production is very sensitive to the cost. Binocular stereo vision has advantages over other sensors because of low cost, high sampling frequency, large amount of data obtaining ability, low-power consumption, and light weight, which is widely used in the all-production links in smart agriculture [2, 3]. However, in most condition of agricultural outdoor working environment, optical sensors and This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…In 2017, Zeng et al proposed a super-resolution reconstruction method to improve the convolutional neural network and apply it to a single image. It is a new network that combines a dense residual network and deconvolutional network [20,21]. In terms of processing single images and multi-level processing, it is easier to perform image reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Research on Driver Status Recognition System of Intelligent Vehicle Terminal Based on Deep Learning

Xu¹,

Peng²,

Wang³

2021

WEVJ

View full text Add to dashboard Cite

Automobile safety driving technology is a hot topic in today’s society, which is very significant to the social transportation system. Vehicle driving behavior monitoring is the foundation and core of safe driving techniques. The research on existing vehicle safety technology can not only improve the understanding of current safe driving research progress, but also provide reference for future researchers. This paper proposes a state recognition system based on a three-dimensional convolutional neural network, which can identify several improper states frequently encountered by drivers during driving, including drinking, making phone calls, and smoking, and can also issue alarm interventions. The system takes the collected continuous video frame information as the input of the three-dimensional convolutional network, carries out multi-level feature extraction and spatio-temporal information fusion, and identifies the driver state according to the extracted spatio-temporal features. The state is judged by the facial feature points of the video stream, and the design of the video surveillance driver state recognition system is completed. Then, the driver status recognition is improved and optimized, and finally, the actual deployment of the driver status recognition system on the mobile terminal is completed. A large number of experimental results show that the driver status recognition system proposed in this paper has achieved upper identification accuracy.

show abstract

Multispectral image enhancement based on illuminance-reflection imaging model and morphology operation

Cited by 9 publications

References 21 publications

Nighttime Image Stitching Method Based on Image Decomposition Enhancement

Nighttime Image Stitching Method Based on Image Decomposition Enhancement

Hawk‐eye‐inspired perception algorithm of stereo vision for obtaining orchard 3D point cloud navigation map

Research on Driver Status Recognition System of Intelligent Vehicle Terminal Based on Deep Learning

Contact Info

Product

Resources

About