67 research outputs found

    Multi-sensor Image Data Fusion based on Pixel-Level Weights of Wavelet and the PCA Transform

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    Abstract -The goal of image fusion is to create new images that are more suitable for the purposes of human visual perception, object detection and target recognition. For Automatic Target Recognition (ATR), we can use multi-sensor data including visible and infrared images to increase the recognition rate. In this paper, we propose a new multiresolution data fusion scheme based on the principal component analysis (PCA) transform and the pixel-level weights wavelet transform including thermal weights and visual weights. In order to get a more ideal fusion result, a linear local mapping which based on the PCA is used to create a new "origin" image of the image fusion. We use multiresolution decompositions to represent the input images at different scales, present a multiresolution/ multimodal segmentation to partition the image domain at these scales. The crucial idea is to use this segmentation to guide the fusion process. Physical thermal weights and perceptive visual weights are used as segmentation multimodals. Daubechies Wavelet is choosen as the Wavelet Basis. Experimental results confirm that the proposed algorithm is the best image sharpening method and can best maintain the spectral information of the original infrared image. Also, the proposed technique performs better than the other ones in the literature, more robust and effective, from both subjective visual effects and objective statistical analysis results

    Intracoronary artery retrograde thrombolysis combined with percutaneous coronary interventions for ST-segment elevation myocardial infarction complicated with diabetes mellitus: A case report and literature review

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    BackgroundThe management of a large thrombus burden in patients with acute myocardial infarction and diabetes is still a worldwide problem.Case presentationA 74-year-old Chinese woman presented with ST-segment elevation myocardial infarction (STEMI) complicated with diabetes mellitus and hypertension. Angiography revealed massive thrombus formation in the mid-segment of the right coronary artery leading to vascular occlusion. The sheared balloon was placed far from the occlusion segment and urokinase (100,000 u) was administered for intracoronary artery retrograde thrombolysis, and thrombolysis in myocardial infarction (TIMI) grade 3 blood flow was restored within 7 min. At last, one stent was accurately implanted into the culprit’s vessel. No-reflow, coronary slow flow, and reperfusion arrhythmia were not observed during this process.ConclusionIntracoronary artery retrograde thrombolysis (ICART) can be effectively and safely used in patients with STEMI along with diabetes mellitus and hypertension, even if the myocardial infarction exceeds 12 h (REST or named ICART ClinicalTrials.gov number, ChiCTR1900023849)

    Construction Strategy of Regional Plant Landscape in Urban Gardens

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    By sorting out the problems existing in the construction of plant landscapes in urban gardens, the designer plans tree species, characteristics, spaces, colours, etc. from the perspective of ecosystem balance, applies local rich native plants, and explores the construction strategies of regional plant landscapes in urban gardens. Taking the city of Guilin as an example, the article analyses the construction features of the band green landscape of the two rivers and four lakes scenic spot, summarizes the construction characteristics of the regional plant landscape of Guilin, promotes the construction of an ecological garden city, and meets the people’s beautiful environmental needs

    SFRE-Net: Scattering Feature Relation Enhancement Network for Aircraft Detection in SAR Images

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    Aircraft detection in synthetic aperture radar (SAR) images is a challenging task due to the discreteness of aircraft scattering characteristics, the diversity of aircraft size, and the interference of complex backgrounds. To address these problems, we propose a novel scattering feature relation enhancement network (SFRE-Net) in this paper. Firstly, a cascade transformer block (TRsB) structure is adopted to improve the integrity of aircraft detection results by modeling the correlation between feature points. Secondly, a feature-adaptive fusion pyramid structure (FAFP) is proposed to aggregate features of different levels and scales, enable the network to autonomously extract useful semantic information, and improve the multi-scale representation ability of the network. Thirdly, a context attention-enhancement module (CAEM) is designed to improve the positioning accuracy in complex backgrounds. Considering the discreteness of scattering characteristics, the module uses a dilated convolution pyramid structure to improve the receptive field and then captures the position of the aircraft target through the coordinate attention mechanism. Experiments on the Gaofen-3 dataset demonstrate the effectiveness of SFRE-Net with a precision rate of 94.4% and a recall rate of 94.5%

    SFRE-Net: Scattering Feature Relation Enhancement Network for Aircraft Detection in SAR Images

    No full text
    Aircraft detection in synthetic aperture radar (SAR) images is a challenging task due to the discreteness of aircraft scattering characteristics, the diversity of aircraft size, and the interference of complex backgrounds. To address these problems, we propose a novel scattering feature relation enhancement network (SFRE-Net) in this paper. Firstly, a cascade transformer block (TRsB) structure is adopted to improve the integrity of aircraft detection results by modeling the correlation between feature points. Secondly, a feature-adaptive fusion pyramid structure (FAFP) is proposed to aggregate features of different levels and scales, enable the network to autonomously extract useful semantic information, and improve the multi-scale representation ability of the network. Thirdly, a context attention-enhancement module (CAEM) is designed to improve the positioning accuracy in complex backgrounds. Considering the discreteness of scattering characteristics, the module uses a dilated convolution pyramid structure to improve the receptive field and then captures the position of the aircraft target through the coordinate attention mechanism. Experiments on the Gaofen-3 dataset demonstrate the effectiveness of SFRE-Net with a precision rate of 94.4% and a recall rate of 94.5%

    GF-Detection: Fusion with GAN of Infrared and Visible Images for Vehicle Detection at Nighttime

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    Vehicles are important targets in the remote sensing applications and nighttime vehicle detection has been a hot study topic in recent years. Vehicles in the visible images at nighttime have inadequate features for object detection. Infrared images retain the contours of vehicles while they lose the color information. Thus, it is valuable to fuse infrared and visible images to improve the vehicle detection performance at nighttime. However, it is still a challenge to design effective fusion models due to the complexity of visible and infrared images. In order to improve vehicle detection performance at nighttime, this paper proposes a fusion model of infrared and visible images with Generative Adversarial Networks (GAN) for vehicle detection named GF-detection. GAN is utilized in the image reconstruction and introduced in the image fusion recently. To be specific, to exploit more features for the fusion, GAN is utilized to fuse the infrared and visible images via the image reconstruction. The generator fuses the image features and detection features, and then generates the reconstructed images for the discriminator to classify. Two branches, visible and infrared branches, are designed in the GF-detection model. Different feature extraction strategies are conducted according to the variance of the visible and infrared images. Detection features and self-attention mechanism are added to the fusion model aiming to build a detection task-driven fusion model of infrared and visible images. Extensive experiments based on nighttime images are conducted to demonstrate the effectiveness of the proposed fusion model in night vehicle detection

    GF-Detection: Fusion with GAN of Infrared and Visible Images for Vehicle Detection at Nighttime

    No full text
    Vehicles are important targets in the remote sensing applications and nighttime vehicle detection has been a hot study topic in recent years. Vehicles in the visible images at nighttime have inadequate features for object detection. Infrared images retain the contours of vehicles while they lose the color information. Thus, it is valuable to fuse infrared and visible images to improve the vehicle detection performance at nighttime. However, it is still a challenge to design effective fusion models due to the complexity of visible and infrared images. In order to improve vehicle detection performance at nighttime, this paper proposes a fusion model of infrared and visible images with Generative Adversarial Networks (GAN) for vehicle detection named GF-detection. GAN is utilized in the image reconstruction and introduced in the image fusion recently. To be specific, to exploit more features for the fusion, GAN is utilized to fuse the infrared and visible images via the image reconstruction. The generator fuses the image features and detection features, and then generates the reconstructed images for the discriminator to classify. Two branches, visible and infrared branches, are designed in the GF-detection model. Different feature extraction strategies are conducted according to the variance of the visible and infrared images. Detection features and self-attention mechanism are added to the fusion model aiming to build a detection task-driven fusion model of infrared and visible images. Extensive experiments based on nighttime images are conducted to demonstrate the effectiveness of the proposed fusion model in night vehicle detection
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