An improved image enhancement framework based on multiple attention mechanism

Chen, Qili; Fan, Junfang; Chen, Wenbai

doi:10.1016/j.displa.2021.102091

Cited by 18 publications

(6 citation statements)

References 23 publications

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“…In recent years, the attention mechanism has been widely used [ 15 , 16 , 17 , 18 ]. Its purpose is to enable the network to learn more important things.…”

Section: Related Workmentioning

confidence: 99%

Person Re-Identification Based on Contour Information Embedding

Chen

Zhao

Wang

2023

Sensors

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Person re-identification (Re-ID) plays an important role in the search for missing people and the tracking of suspects. Person re-identification based on deep learning has made great progress in recent years, and the application of the pedestrian contour feature has also received attention. In the study, we found that pedestrian contour feature is not enough in the representation of CNN. On this basis, in order to improve the recognition performance of Re-ID network, we propose a contour information extraction module (CIEM) and a contour information embedding method, so that the network can focus on more contour information. Our method is competitive in experimental data; the mAP of the dataset Market1501 reached 83.8% and Rank-1 reached 95.1%. The mAP of the DukeMTMC-reID dataset reached 73.5% and Rank-1 reached 86.8%. The experimental results show that adding contour information to the network can improve the recognition rate, and good contour features play an important role in Re-ID research.

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“…In recent years, the attention mechanism has been widely used [ 15 , 16 , 17 , 18 ]. Its purpose is to enable the network to learn more important things.…”

Section: Related Workmentioning

confidence: 99%

Person Re-Identification Based on Contour Information Embedding

Chen

Zhao

Wang

2023

Sensors

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“…The Squeeze and Excitation Network(SENet)module obtains the importance of each channel in the feature graph, uses the importance to assign weights to the features, makes the network focus on certain feature channels and improves the channels useful for the current task, inhibition less important channels, captures the hidden states at each time point by introducing neural network and calculate the weight, output the results after weighting [34][35][36]. The network diagram of SENet is shown in Figure 2 (different colours represent different weights).…”

Section: Proposed 3dsecnn Modelmentioning

confidence: 99%

Research on identification and classification of grassland forage based on deep learning and attention mechanisms

Liu

Zhao

et al. 2023

IET Image Processing

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Grassland is an important resource for China's economic development and the main economic source of animal husbandry. The identification and classification of grassland forage is an important part of the improvement of forage varieties and the monitoring of germplasm resources, which can fundamentally solve the problems of poor forage quality and low reproduction rate. For the problem of low accuracy of forage identification and classification, the authors put forward a new 3DSECNN model to remove the preprocessing operation and directly study the images. The authors took forage hyperspectral image (HSI) images on the field and built dataset, used 3DSECNN to train the images to improve the classification effect. The outstanding contributions of this paper are: (1) The authors took high‐precision forage HSI images in the field, established a dedicated database of forage HSIs, and expanded the datasets; (2) the process of integrating preprocessing ideas into the network and replacing the traditional method of preprocessing the data and then extracting features; (3) proposing the 3DSECNN model, which adds SENet on the basis of the traditional 3DCNN, strengthens the correlation of the spatial dimension, selects the key features for the classification by calculating the channel weight, inhibits the unimportant information, and achieves the purpose of integrating the preprocessing ideas into the network. The experimental results show the overall accuracy (OA) of 3DSECNN is 94.36%, Precision, Recall, F1‐score, Kappa, and Time also showed good levels. The experimental results prove that the 3DSECNN strengthens the correlation between image channels, enhances the performance ability of features, and provides a new method for the identification and classification research of forage.

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“…In [ 45 ], the attention module is used for low-light image enhancement. In [ 46 ], a new non-local attention module is proposed for low-light image enhancement using multiple exposure image sequences.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Post-Filtering Using Multi-Head Attention and Multiresolution Feature Fusion for Image and Intra-Video Quality Enhancement

Schiopu¹,

Munteanu²

2022

Sensors

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The paper proposes a novel post-filtering method based on convolutional neural networks (CNNs) for quality enhancement of RGB/grayscale images and video sequences. The lossy images are encoded using common image codecs, such as JPEG and JPEG2000. The video sequences are encoded using previous and ongoing video coding standards, high-efficiency video coding (HEVC) and versatile video coding (VVC), respectively. A novel deep neural network architecture is proposed to estimate fine refinement details for full-, half-, and quarter-patch resolutions. The proposed architecture is built using a set of efficient processing blocks designed based on the following concepts: (i) the multi-head attention mechanism for refining the feature maps, (ii) the weight sharing concept for reducing the network complexity, and (iii) novel block designs of layer structures for multiresolution feature fusion. The proposed method provides substantial performance improvements compared with both common image codecs and video coding standards. Experimental results on high-resolution images and standard video sequences show that the proposed post-filtering method provides average BD-rate savings of 31.44% over JPEG and 54.61% over HEVC (x265) for RGB images, Y-BD-rate savings of 26.21% over JPEG and 15.28% over VVC (VTM) for grayscale images, and 15.47% over HEVC and 14.66% over VVC for video sequences.

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An improved image enhancement framework based on multiple attention mechanism

Cited by 18 publications

References 23 publications

Person Re-Identification Based on Contour Information Embedding

Person Re-Identification Based on Contour Information Embedding

Research on identification and classification of grassland forage based on deep learning and attention mechanisms

Deep Learning Post-Filtering Using Multi-Head Attention and Multiresolution Feature Fusion for Image and Intra-Video Quality Enhancement

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