Low Illumination Video Image Enhancement

Li, Zhi; Yang, Jie; Kasabov, Nikola

doi:10.1109/jphot.2020.3010966

Cited by 14 publications

(8 citation statements)

References 25 publications

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“…In future, it is worth comparing our approach with the learning-based techniques, e.g., [9]. This work can also be extended to deal with microscopic images which contain many repetitive structures [21], and videos by additionally enforcing temporal consistency [22].…”

Section: Discussionmentioning

confidence: 99%

Adaptive amplification of image texture boundaries

Wei¹,

Guo²,

Guo³

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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The texture boundaries normally have frequent co-occurrence in natural images, but existing image enhancement techniques for the most parts focus on sharpening the edges, i.e., intensity discontinuities. Moreover, these approaches often suffer from noise over-emphasis and extra artifact production. In this paper, we propose an adaptive texture boundary boosting algorithm. The proposal exploits the co-occurrence filter for dual-layer decomposition and pixel-wise amplification factor calculation for image synthesis. This leads to a various-scale enhancement framework, which allows our method to only highlight those co-occurrent features while avoiding unsalient edges and noise, gradient reversals, as well as halos. Both subjective and objective comparisons using different detail stretching schemes demonstrate the effectiveness of the presented approach.

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Section: Discussionmentioning

confidence: 99%

Adaptive amplification of image texture boundaries

Wei¹,

Guo²,

Guo³

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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show abstract

“…Better use of increasing audio, text, video and other information to assist human production and life has been a concern of scholars at home and abroad [1][2][3][4][5][6][7]. With the development of in-depth learning technology and people's demand for intelligent security, human-computer interaction, shopping mall guidance and other technologies, computer vision technology for pedestrian identity recognition shows great application value [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Identification Method Based on Multi-Scale Feature Learning in Surveillance Video Images

Li¹,

Pang²,

An³

et al. 2022

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With the development of deep learning technology and people's demand for intelligent security, human-computer interaction, shopping guide and other technologies, computer vision technology for pedestrian identification shows great application value. In this paper, pedestrian identification method based on multi-scale feature learning in surveillance video images is studied. Firstly, the deep residual network ResNet and densely connected convolutional network DenseNet are introduced as baseline networks. A model is constructed based on hybrid hourglass network module, enhanced weighted feature pyramid fusion network module and post-processing module. The loss function is designed, which is unified with other traditional models, and the optimization objective of the loss function is respectively corresponding to three parts, namely, the prediction error of corresponding center point, the prediction error of offset and the prediction error of bounding box size. The experimental results verify the effectiveness of the proposed model.

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“…Li et al. [4] proposed a low‐light video image enhancement algorithm. They used the HSV colour space to replace the RGB space and used the wavelet image fusion method to enhance the details of the video image.…”

Section: Introductionmentioning

confidence: 99%

“…However, the LIME method brightens the underexposed areas, but at the same time makes the originally well-exposed or overexposed areas worse. Li et al [4] proposed a low-light video image enhancement algorithm. They used the HSV colour space to replace the RGB space and used the wavelet image fusion method to enhance the details of the video image.…”

Section: Introductionmentioning

confidence: 99%

Joint transformer progressive self‐calibration network for low light enhancement

Fan

Hua

et al. 2022

IET Image Processing

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When the lighting conditions are poor and the environmental light is weak, the image captured by the imaging device often has lower brightness and is accompanied by a lot of noise. The paper designs a progressive self‐calibration network model (PSCNet) for recovering high‐quality low‐light‐enhanced images. First, shallow features in low‐light images can be better focused and extracted with the help of attention mechanism. Next, the feature mapping is passed to the encoder and decoder modules, where the transformer and encoder‐decoder jump connection structures can be better combined with the semantic information of the context to learn rich deep feature information. Finally, the self‐calibration module can adaptively cascade the features decoded by the decoder and input them into the residual attention module quickly and accurately. Meanwhile, the LBP features of the image are also fused into the feature information of the residual attention module to enhance the detailed texture information of the image. Qualitative analysis and quantitative comparison of a large number of experimental results show that this method outperforms existing methods.

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Low Illumination Video Image Enhancement

Cited by 14 publications

References 25 publications

Adaptive amplification of image texture boundaries

Adaptive amplification of image texture boundaries

Pedestrian Identification Method Based on Multi-Scale Feature Learning in Surveillance Video Images

Joint transformer progressive self‐calibration network for low light enhancement

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