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Single image defogging via multi-exposure image fusion and detail enhancement

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Journal of Safety Science and Resilience 2024年第1期5卷 37-46页

作者： Wenjing Mao Dezhi Zheng Minze Chen Juqiang Chen School of Information and Electronics Advanced Research Institute of Multidisciplinary ScienceBeijing Institute of TechnologyBeijing100081China Chinese Aeronautical Establishment Beijing100012China

Outdoor cameras play an important role in monitoring security and social governance.As a common weather phenomenon,haze can easily affect the quality of camera shooting,resulting in loss and distortion of image details.This paper proposes an improved multi-exposure image fusion defogging technique based on the artificial multi-exposure image fusion(AMEF)algorithm.First,the foggy image is adaptively exposed,and the fused image is subsequently obtained via multiple exposures.The fusion weight is determined by the saturation,contrast,and brightness.Finally,the image fused by a multi-scale Laplacian algorithm is enhanced with simple adaptive details to obtain a clearer defogging image.It is subjectively and objectively verified that this algorithm can obtain more image details and distinct picture colors without a priori information,effectively improving the defogging ability.

关键词： Image defogging multi-scale fusion Laplacian pyramid Adaptive detail enhancement

Face anti-spoofing based on multi-modal and multi-scale features fusion

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The Journal of China Universities of Posts and Telecommunications 2022年第6期29卷 73-82页

作者： Kong Chao Ou Weihua Gong Xiaofeng Li Weian Han Jie Yao Yi Xiong Jiahao School of Big Data and Computer Science Guizhou Normal UniversityGuiyang 550025China Special Key Laboratory of Artificial Intelligence and Intelligent Control of Guizhou Province Guizhou Institute of TechnologyGuiyang 550003China Guizhou Science and Technology Information Center Department of Science and Technology of Guizhou ProvinceGuiyang 550002China

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network(CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion(MMFF) is proposed. Specifically, first residual network(Resnet)-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network(FPN), finally squeeze-and-excitation fusion(SEF) module and self-attention network(SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.

关键词： face anti-spoofing multi-modal fusion multi-scale fusion self-attention network(SAN) feature pyramid network(FPN)

Clothing Parsing Based on multi-scale fusion and Improved Self-Attention Mechanism

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Journal of Donghua University(English Edition) 2023年第6期40卷 661-666页

作者：陈诺王绍宇陆然李文萱覃志东石秀金 College of Computer Science and Technology Donghua UniversityShanghai 201620China

Due to the lack of long-range association and spatial location information,fine details and accurate boundaries of complex clothing images cannot always be obtained by using the existing deep learning-based methods.This paper presents a convolutional structure with multi-scale fusion to optimize the step of clothing feature extraction and a self-attention module to capture long-range association information.The structure enables the self-attention mechanism to directly participate in the process of information exchange through the down-scaling projection operation of the multi-scale framework.In addition,the improved self-attention module introduces the extraction of 2-dimensional relative position information to make up for its lack of ability to extract spatial position features from clothing images.The experimental results based on the colorful fashion parsing dataset(CFPD)show that the proposed network structure achieves 53.68%mean intersection over union(mIoU)and has better performance on the clothing parsing task.

关键词： clothing parsing convolutional neural network multi-scale fusion self-attention mechanism vision Transformer

Nighttime image dehazing using color cast removal and dual path multi-scale fusion strategy

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Frontiers of Computer Science 2022年第4期16卷 147-159页

作者： Bo Wang Li Hu Bowen Wei Zitong Kang Chongyi Li School of Physics and Electronic-Electrical Engineering Ningxia UniversityYinchuan750021China School of Computer Science and Engineering Nanyang Technological UniversitySingapore639798Singapore

Nighttime image dehazing aims to remove the effect of haze on the images captured in nighttime,which however,raises new challenges such as severe color distortion,more complex lighting conditions,and lower contrast.Instead of estimating the transmission map and atmospheric light that are difficult to be accurately acquired in nighttime,we propose a nighttime image dehazing method composed of a color cast removal and a dual path multi-scale fusion algorithm.We first propose a human visual system(HVS)inspired color correction model,which is effective for removing the color deviation on nighttime hazy images.Then,we propose to use dual path strategy that includes an underexposure and a contrast enhancement path for multi-scale fusion,where the weight maps are achieved by selecting appropriate exposed areas under Gaussian pyramids.Extensive experiments demonstrate that the visual effect of the hazy nighttime images in real-world datasets can be significantly improved by our method regarding contrast,color fidelity,and visibility.In addition,our method outperforms the state-of-the-art methods qualitatively and quantitatively.

关键词： nighttime image dehazing color cast removal dual path multi-scale fusion

DCAU-Net:dense convolutional attention U-Net for segmentation of intracranial aneurysm images

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 99-114页

作者： Wenwen Yuan Yanjun Peng Yanfei Guo Yande Ren Qianwen Xue College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China The Department of Radiology the Affiliated Hospital of Qingdao UniversityQingdao 266000China Qingdao Maternal&Child Health and Family Planning Service Center Qingdao 266034China

Segmentation of intracranial aneurysm images acquired using magnetic resonance angiography(MRA)is essential for medical auxiliary treatments,which can effectively prevent subarachnoid hemorrhages.This paper proposes an image segmentation model based on a dense convolutional attention U-Net,which fuses deep and rich semantic information with shallow-detail information for adaptive and accurate segmentation of MRA-acquired aneurysm images with large size differences.The U-Net model serves as a backbone,combining dense block and convolution block attention module(CBAM).The dense block is composed of a batch normalization layer,an randomly rectified linear unit activation function,and a convolutional layer,for mitigation of vanishing gradients,for multiplexing of aneurysm features,and for improving the network training efficiency.The CBAM is composed of a channel attention module and a spatial attention module,improving the segmentation performance of feature discrimination and enhancing the acquisition of key feature information.Owing to the large variation of aneurysm sizes,multi-scale fusion is performed during up-sampling,for adaptive segmentation of MRA-acquired aneurysm images.The model was tested on the MICCAI 2020 ADAM dataset,and its generalizability was validated on the clinical aneurysm dataset(aneurysm sizes:7 mm)supplied by the Affiliated Hospital of Qingdao University.A good clinical application segmentation performance was demonstrated.

关键词： Deep learning Intracranial aneurysm segmentation Magnetic resonance angiography multi-scale fusion

Underwater Diver Image Enhancement via Dual-Guided Filtering

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Computer Modeling in Engineering & Sciences 2022年第5期131卷 1063-1081页

作者： Jingchun Zhou Taian Shi Weishi Zhang Weishen Chu College of Information Science and Technology Dalian Maritime UniversityDalian116026China Department of Computer and Information Science University of PennsylvaniaPhiladelphiaPA 19104USA

The scattering and absorption of light propagating underwater cause the underwater images to present lowcontrast,color deviation,and loss of details,which in turn make human posture recognition challenging.To address these issues,this study introduced the dual-guided filtering technique and developed an underwater diver image improvement method.First,the color distortion of the underwater diver image was solved using white balance technology to obtain a color-corrected image.Second,dual-guided filtering was applied to the white balanced image to correct the distorted color and enhance its details.Four feature weight maps of the two images were then calculated,and two normalizedweightmapswere constructed formulti-scale fusion using normalization.To better preserve the obtained image details,the fusion image was histogram-stretched to obtain the final enhanced result.The experimental results validated that this method has improved the accuracy of underwater human posture recognition.

关键词： multi-scale fusion image enhancement guided filter underwater diver images