- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2019 Chinese Control And Decision Conference (CCDC) - Nanchang, China (2019.6.3-2019.6.5)] 2019 Chinese Control And Decision Conference (CCDC) - A novel Image Fusion Framework based on Non-Subsampled Shearlet Transform (NSST) Domain
摘要： As an effective means to integrate the information contained in multiple images in different ways, multi-model image fusion provides more comprehensive and sophisticated information for modern medical diagnosis, remote sensing, video surveillance and other ?elds. Based on non-subsampled shearlet transform (NSST) domain, this paper proposed an general image fusion framework to well preserve image energy and detail. It can well solve the problem of limited direction in image decomposition. This fusion method applies NSST to decomposition source images into high-pass and low-pass subbands. Sum Modi?ed-laplacian(SML) is employed to fused high-pass bands. A weighted sum of weighted local energy (WLE) and a modi?ed Laplacian (WSEML) based on eight neighborhoods are used as a fusion strategy to fuse low-frequency components. Finally, employing NSST inverse transform to reconstruct the fused high frequency and low frequency components. The experimental results indicate that the framework can save more image details and improve the quality of fused images.
关键词： image fusion,non-subsampled shearlet transform (NSST),multi-model images,SML
更新于2025-09-16 10:30:52
-
[ACM Press the 2019 International Conference - Wuhan, Hubei, China (2019.07.12-2019.07.13)] Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science - AICS 2019 - A Novel Infrared and Visible Image Fusion Using Low-rank Representation and Simplified Dual Channel Pulse Coupled Neural Network
摘要： This paper establishes a novel fusion scheme for infrared (IR) and visual (VI) images via low-rank representation (LRR), total variation (TV) model and simplified dual channel pulse coupled neural network (S-DPCNN) to effectively extract the major and salient information, which address some problems in existing fusion methods low-contrasting such as blurry edge, heterogeneous and information redundancy. The first step of the proposed method is to extract the valuable features of the IR images based on frequency tuned based- LRR (FT-LRR) algorithm aims to separate the corresponding salient regions and backgrounds. Furthermore, we adopt a choose-maximum rule to retain the significance information of the source images to the maximum extent for the salient region. For the background, IR and VI images are decomposed into a low-pass coefficient and a series of high-pass coefficients using non-subsampled shearlet transform (NSST). Then, the TV model is utilized to fuse the low-pass coefficient, in the meanwhile, the modified average gradient (MAG) is used to stimulate the S-DPCNN which aims to fuse high-pass coefficients. The fused background is obtained by taking inverse NSST. Finally, the robust fused image is generated by adding the fused salient region and background. Large amounts of experiment results and metrics demonstrate that the proposed framework exhibits good visual performance and has obvious superiorities over other state-of-the-art methods in both subjective and objective evaluation.
关键词： Image Fusion,Pulse Coupled Neural Network,Total Variation Model,Low-rank Representation
更新于2025-09-16 10:30:52
-
Research of Multimodal Medical Image Fusion Based on Parameter-Adaptive Pulse-Coupled Neural Network and Convolutional Sparse Representation
摘要： Visual effects of medical image have a great impact on clinical assistant diagnosis. At present, medical image fusion has become a powerful means of clinical application. The traditional medical image fusion methods have the problem of poor fusion results due to the loss of detailed feature information during fusion. To deal with it, this paper proposes a new multimodal medical image fusion method based on the imaging characteristics of medical images. In the proposed method, the non-subsampled shearlet transform (NSST) decomposition is first performed on the source images to obtain high-frequency and low-frequency coefficients. The high-frequency coefficients are fused by a parameter-adaptive pulse-coupled neural network (PAPCNN) model. The method is based on parameter adaptive and optimized connection strength β adopted to promote the performance. The low-frequency coefficients are merged by the convolutional sparse representation (CSR) model. The experimental results show that the proposed method solves the problems of difficult parameter setting and poor detail preservation of sparse representation during image fusion in traditional PCNN algorithms, and it has significant advantages in visual effect and objective indices compared with the existing mainstream fusion algorithms.
关键词： multimodal,medical image fusion,parameter-adaptive pulse-coupled neural network,convolutional sparse representation,non-subsampled shearlet transform
更新于2025-09-16 10:30:52
-
[IEEE 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) - Bangalore (2018.7.10-2018.7.12)] 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) - Image Fusion Using Convolutional Neural Network with Bilateral Filtering
摘要： Image fusion is a method of combining source images taken from the same scene. A deep convolutional neural network (CNN) is used in this paper to extract the high frequency details from the two source images. A focus map is generated after the several convolution and max-pooling layers which contains the clarity information of the source images. A fixed threshold is applied to the focus map to generate a binary segmented map which correctly classifies the pixels belonging to the focused regions. The results of binary segmentation contain some mis-classified pixels which is improved by applying a small region removal strategy to get the initial decision map. The proposed bilateral filter is a very efficient edge-preserving filter which smoothen the regions around the boundaries of the obtained decision map. The pixel-wise weighted average strategy is calculated to get the fused image with high visual quality. Experimental results show that the proposed CNN-based method produces more natural effect of the fused image.
关键词： Convolutional neural network,bilateral filter,Image fusion
更新于2025-09-11 14:15:04
-
Local sparseness and image fusion for defect inspection in eddy current pulsed thermography
摘要： Defect feature extraction and analysis based on eddy current pulsed thermography (ECPT) technique is a research focus in non-destructive testing area. In this paper, a new feature extraction method based on thermography is proposed to enhance quantitative defect information. The proposed method included entropy-based image selection, local (element-wise) sparse and low rank decomposition (LSLD) and image fusion can increase the contrast of defect area and background and extract more useful defect features than other two common feature extraction algorithms in ECPT. The experiments including comparison results are provided to demonstrate the capabilities and benefits of the proposed algorithm. More meaningful defect information of the experimental specimens is reserved from raw ECPT data and background is suppressed severely compared with other feature extraction algorithms.
关键词： local sparse,eddy current,image fusion,non-destructive evaluation,thermography
更新于2025-09-11 14:15:04
-
[IEEE 2018 2nd IEEE Advanced Information Management,Communicates, Electronic and Automation Control Conference (IMCEC) - Xi'an (2018.5.25-2018.5.27)] 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC) - Fusion Method for Infrared and Visible Light Images Utilizing SWT and NSCT
摘要： In allusion to the feature information of image direction can not effectively expressed by stationary wavelet transform(SWT) and the local characteristic changes for images can not be expressed clearly by NSCT transform, a novel image fusion method combining stationary wavelet transform with NSCT is proposed in this paper. Firstly, the infrared image and visible light image are decomposed into low and high frequency subbands by NSCT. Then the low frequency subbands are fused based on SWT and the high frequency subbands are fused utilizing absolute value criterion. The fused image is obtained by inverse NSCT transform. The experimental results show that the proposed method can better improve image visual effect, and the objective indices such as standard deviation, entropy, gradient and spatial frequency are increased significantly.
关键词： SWT transform,Image fusion,NSCT transform
更新于2025-09-11 14:15:04
-
Fusion algorithm of infrared and visible images based on frame difference detection technology and area feature
摘要： A kind of fusion algorithm of infrared and visible images based on frame difference detection technology and area feature was proposed to increase the fusion quality of the infrared image and reduce complexity. Firstly, frame difference method was designed to complete detection on objectives in infrared images so as to conduct objective clustering and image segmentation; information among frames is used to complete accurate locating of objective; then different fusion rules were designed to complement effective information of visible light and infrared images as possible and complete image fusion according to features of objective area. The complexity of fusion algorithm in this thesis was analyzed theoretically. Meanwhile, fusion experiment was executed on two conditions of unmovable and observable objective in image of visible light and infrared light and movable and observable objective in image of visible light and infrared light; experiment result indicates that proposed technology has higher fusion quality and the fusion image can accurately reflect objective and background compared with current image fusion technology.
关键词： area feature,Image fusion,objective area segmentation,frame difference detection,infrared and visible light image
更新于2025-09-10 09:29:36
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fusenet: End- to-End Multispectral Vhr Image Fusion and Classification
摘要： Classification of very high resolution (VHR) satellite images faces two major challenges: 1) inherent low intra-class and high inter-class spectral similarities and 2) mismatching resolution of available bands. Conventional methods have addressed these challenges by adopting separate stages of image fusion and spatial feature extraction steps. These steps, however, are not jointly optimizing the classification task at hand. We propose a single-stage framework embedding these processing stages in a multiresolution convolutional network. The network, called FuseNet, aims to match the resolution of the panchromatic and multispectral bands in a VHR image using convolutional layers with corresponding downsampling and upsampling operations. We compared FuseNet against the use of separate processing steps for image fusion, such as pansharpening and resampling through interpolation. We also analyzed the sensitivity of the classification performance of FuseNet to a selected number of its hyperparameters. Results show that FuseNet surpasses conventional methods.
关键词： image fusion,Convolutional networks,deep learning,land cover classification,VHR image
更新于2025-09-10 09:29:36
-
Unified Image Fusion Framework with Learning-Based Application-Adaptive Importance Measure
摘要： This paper presents a novel unified image fusion framework based on an application-adaptive importance measure. In the proposed framework, an important area is selected using the importance measure obtained for each image type in each application. The key is to learn this application-adaptive importance measure that can select the important area irrespective of the input image type without manually designing the algorithm for each application. Then, the fused intensity is generated using Poisson image reconstruction. Experimental results demonstrate that the proposed framework is effective for various applications including depth-perceptible image enhancement, temperature-preserving image fusion, and haze removal.
关键词： RGBD,NIR,Image fusion,FIR,Image enhancement
更新于2025-09-10 09:29:36
-
[IEEE 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) - Huhhot, China (2018.9.14-2018.9.16)] 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) - Multi-directional Laplacian Pyramid Image Fusion Algorithm
摘要： The traditional Laplacian pyramid fusion method cannot effectively reflect the edge information of the fused image and the poor contrast. A Laplacian pyramid algorithm with multi-directional joint averaging is proposed. First, the input image is decomposed using the Laplacian pyramid. Secondly, for the high frequency coefficient, the rule of extracting the multi- directional feature map of the joint average fusion is adopted; the low frequency coefficient adopts the regional average gradient adaptive weighted fusion rule. Finally, the fused image is obtained by the reconstruction process of the inverse Laplacian the pyramid transform. Experimental results show that algorithm can effectively reflect the edge information of the fused image and rich background details.
关键词： image fusion,image processing,multidirectional feature,Laplacian pyramid
更新于2025-09-10 09:29:36