- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Investigation of Remote Sensing Image Fusion Strategy Applying PCA to Wavelet Packet Analysis Based on IHS Transform
摘要： Further exploration of wavelet packet analysis (WPA) in the area of image fusion has been a hot topic. It is a strategy to combine WPA with such other transforms as intensity–hue–saturation (IHS), principle component analysis (PCA) for image fusion between the panchromatic (PAN) and the multispectral (MS) image. The paper puts forward a distinct fusion method. Its main idea can be stated as three steps. Firstly, intensity component is derived from IHS model of the image after an MS image is transformed from RGB to IHS. Secondly, intensity component and a matched PAN image are decomposed by WPA at the second scale, respectively. The innovational concept with two aspects is applying PCA theory to merge wavelet packet coefficients. One is to detect edge and produce self-adaptive weighted ratios for low-frequency band. The other is to yield another weighted coefficients for high-frequency bands based on standard deviation. Lastly, the new intensity component created by implementing inverse WPA, matching with hue and saturation reserved, makes up a color composition. A fused image is produced when carrying out transformation from IHS to RGB for the composition. It turns out that the presented fusion strategy is effective with experiments.
关键词： Intensity–hue–saturation (IHS),Image fusion,PCA-based fusion rule,Principle component analysis (PCA),Wavelet packet analysis (WPA)
更新于2025-09-23 15:23:52
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FusionCNN: a remote sensing image fusion algorithm based on deep convolutional neural networks
摘要： In remote sensing image fusion field, traditional algorithms based on the human-made fusion rules are severely sensitive to the source images. In this paper, we proposed an image fusion algorithm using convolutional neural networks (FusionCNN). The fusion model implicitly represents a fusion rule whose inputs are a pair of source images and the output is a fused image with end-to-end property. As no datasets can be used to train FusionCNN in remote sensing field, we constructed a new dataset from a natural image set to approximate MS and Pan images. In order to obtain higher fusion quality, low frequency information of MS is used to enhance the Pan image in the pre-processing step. The method proposed in this paper overcomes the shortcomings of the traditional fusion methods in which the fusion rules are artificially formulated, because it learns an adaptive strong robust fusion function through a large amount of training data. In this paper, Landsat and Quickbird satellite data are used to verify the effectiveness of the proposed method. Experimental results show that the proposed fusion algorithm is superior to the comparative algorithms in terms of both subjective and objective evaluation.
关键词： Convolutional neural networks,Deep learning,Remote sensing image fusion,Image enhancement
更新于2025-09-23 15:23:52
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[Lecture Notes in Computational Vision and Biomechanics] Computer Aided Intervention and Diagnostics in Clinical and Medical Images Volume 31 || A Hybrid Fusion of Multimodal Medical Images for the Enhancement of Visual Quality in Medical Diagnosis
摘要： In the ?eld of medical imaging, Multimodal Medical Image Fusion (MIF) is a method of extracting complementary information from diverse source images from different modalities such as Magnetic Resonance Imaging, Computed Tomography, Single Photon Emission Computed Tomography, and Positron Emission Tomography and coalescing them into a resultant image. Image fusion of multimodal medical images is the present-day studies in the ?eld of medical imaging, biomedical research, and radiation medicine and is widely familiar by medical and engineering ?elds. In medical image fusion, single method of fusion is not pro?cient as it always lags in information while comparing with other available techniques. Hence, fusion for hybrid image is used to perform the image processing by applying multiple fusion rules. The integration of these results was obtained together as a single image. In proposed system, Shearlet Transform (ST) and Principal Component Analysis (PCA) are used to apply integrated fusion. The fusion technique is applied for CT that is Computed Tomography and Magnetic Resonance Imaging (MRI) images, where these images are ?rst transformed using the Shearlet Transform and PCA is applied to the transformed images. Finally, the fusion image is acquired using Inverse Shearlet transform (IST). The proposed system performance is evaluated by using speci?c metrics, and it is demonstrated that the outcome of proposed integrated fusion performs better when compared to existing fusion techniques.
关键词： Image fusion,Medical image,Shearlet Transform (ST),Principal Component Analysis (PCA)
更新于2025-09-23 15:23:52
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Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology
摘要： Sclerotinia sclerotiorum, one of the major diseases infecting oilseed rape leaves, has seriously affected crop yield and quality. In this study, an indoor unmanned aerial vehicle (UAV) low-altitude remote sensing simulation platform was built for disease detection. Thermal, multispectral and RGB images were acquired before and after being artificially inoculated with Sclerotinia sclerotiorum on oilseed rape leaves. New image registration and fusion methods based on scale-invariant feature transform (SIFT) were presented to construct a fused database using multi-model images. The changes of temperature distribution in different sections of infected areas were analyzed by processing thermal images, the maximum temperature difference (MTD) on a single leaf reached 1.7 degrees Celsius 24 h after infection. Four machine learning models were established using thermal images and fused images respectively, including support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN) and na?ve Bayes (NB). The results demonstrated that the classification accuracy was improved by 11.3% after image fusion, and the SVM model obtained a classification accuracy of 90.0% on the task of classifying disease severity. The overall results indicated the UAV low-altitude remote sensing simulation platform equipped with multi-sensors could be used to early detect Sclerotinia sclerotiorum on oilseed rape leaves.
关键词： machine learning,multispectral technology,oilseed rape,image fusion,thermal imaging technology,Sclerotinia sclerotiorum
更新于2025-09-23 15:23:52
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Multifocus image fusion scheme based on discrete cosine transform and spatial frequency
摘要： Multifocus images are different images of the same scene captured with different focus in the cameras. These images when considered individually may not give good quality. Hence to obtain a good quality image, this work proposes an algorithm for fusing multifocus images using Discrete Cosine Transform and spatial frequency. The proposed algorithm works for fusing any number of images. The second step calculates the average and maximum of all the source images and reduces the source images to be processed as two. Then Discrete Cosine Transform (DCT) is applied over the two input images. Min-Max normalization is done on the DCT coefficients and fusion is done using spatial frequency. Inclusion of the second step of the proposed algorithm in some existing algorithms such as Stationary Wavelet Transform, Principal Component Analysis and spatial fusion improves the performance. The metrics used for evaluation proves that the proposed algorithm gives better results than the other algorithms using DCT and state of the art techniques.
关键词： DCT,Min-Max normalization,Image fusion,Spatial frequency
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) - Aristi Village, Zagorochoria, Greece (2018.6.10-2018.6.12)] 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) - MindCamera: Interactive Image Retrieval and Synthesis
摘要： Composing a realistic picture according to the mind is tough work for most people. It is not only a complex operation but also a creation process from nonexistence to existence. Therefore, the core of this problem is to provide rich existing materials for stitching. We present an interactive sketch-based image retrieval and synthesis system, MindCamera. Compared with existing methods, it can use images of daily scenes as the dataset and proposes a sketch-based scene image retrieval model. Furthermore, MindCamera can blend the target object in the gradient domain to avoid the visible seam, and it introduces alpha matting to realize real-time foreground object extraction and composition. Experiments verify that our retrieval model has higher precision and provides more reasonable and richer materials for users. The practical usage demonstrates that MindCamera allows the interactive creation of complex images, and its final compositing results are natural and realistic.
关键词： image fusion,image retrieval,image segmentation
更新于2025-09-23 15:22:29
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Multi-Focus Image Fusion Based on Multiple Directional LOTs
摘要： This letter proposes an image fusion method which adopts a union of multiple directional lapped orthogonal transforms (DirLOTs). DirLOTs are used to generate symmetric orthogonal discrete wavelet transforms and then to construct a union of unitary transforms as a redundant dictionary with a multiple directional property. The multiple DirLOTs can overcome a disadvantage of separable wavelets to represent images which contain slant textures and edges. We analyse the characteristic of local luminance contrast, and propose a fusion rule based on interscale relation of wavelet coefficients. Relying on the above, a novel image fusion method is proposed. Some experimental results show that the proposed method is able to significantly improve the fusion performance from those with the conventional discrete wavelet transforms.
关键词： image fusion,multiple DirLOTs,mutual information
更新于2025-09-23 15:22:29
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Multi-level Features Convolutional Neural Network for Multi-focus Image Fusion
摘要： Multi-focus image fusion is an important technique that aims to generate a single clean image by fusing multiple input images. In this paper, we propose a novel multi-level features convolutional neural network (MLFCNN) architecture for image fusion. In the MLFCNN model, all features learned from previous layers are passed to the subsequent layer. Inside every path between the previous layer and the subsequent layer, we add a 1x1 convolution module to reduce the redundancy. In our method, the source images first are fed to our pre-trained MLFCNN model to obtain the initial focus map. Then, the initial focus map is performed by morphological opening and closing operations and followed by a Gaussian filter to obtain the final decision map. Finally, the fused all-in-focus image is generated based on a weighted-sum strategy with the decision map. The experimental results demonstrate that the proposed method outperforms some state-of-the-art image fusion algorithms in terms of both qualitative and objective evaluations.
关键词： convolutional neural network,decision map,multi-focus image fusion,multi-level features
更新于2025-09-23 15:22:29
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Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution
摘要： Hyperspectral images (HSIs) with high spectral resolution only have the low spatial resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution can be obtained with higher spatial resolution. Therefore, fusing the high-spatial-resolution MSI (HR-MSI) with low-spatial-resolution HSI of the same scene has become the very popular HSI super-resolution scheme. In this paper, a novel low tensor-train (TT) rank (LTTR)-based HSI super-resolution method is proposed, where an LTTR prior is designed to learn the correlations among the spatial, spectral, and nonlocal modes of the nonlocal similar high-spatial-resolution HSI (HR-HSI) cubes. First, we cluster the HR-MSI cubes as many groups based on their similarities, and the HR-HSI cubes are also clustered according to the learned cluster structure in the HR-MSI cubes. The HR-HSI cubes in each group are much similar to each other and can constitute a 4-D tensor, whose four modes are highly correlated. Therefore, we impose the LTTR constraint on these 4-D tensors, which can effectively learn the correlations among the spatial, spectral, and nonlocal modes because of the well-balanced matricization scheme of TT rank. We formulate the super-resolution problem as TT rank regularized optimization problem, which is solved via the scheme of alternating direction method of multipliers. Experiments on HSI data sets indicate the effectiveness of the LTTR-based method.
关键词： low tensor-train (TT) rank (LTTR) learning,image fusion,Hyperspectral imaging,superresolution
更新于2025-09-23 15:22:29
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - A Non Local Multifocus Image Fusion Scheme for Dynamic Scenes
摘要： In order to overcome the limited depth of field of usual photographic devices, a common approach is multi-focus image fusion (MFIF). From a stack of images acquired with different focus settings, these methods aim at fusing the content of the images of the stack to produce a final image that is sharp everywhere. Such methods can be very efficient, but when a global geometrical alignment of images is out-of-reach, or when some objects are moving, the final image shows ghosts or other artefacts. In this paper, we propose a generic method to overcome these limitations. We first select a reference image, and then, for each image of the stack, reconstruct an image that shares the geometry of the reference and the sharpness content of the image at hand. The reconstruction is achieved thanks to a specially crafted modification of the PatchMatch algorithm, adapted to blurred images, and to a dedicated postprocessing for correcting reconstruction errors. Then, from the new image stack, MFIF is performed to produce a sharp result. We show the efficiency of the result on a database of challenging cases of hand-held shots containing moving objects.
关键词： focus stacking,computational photography,PatchMatch,non-local methods,Multifocus image fusion
更新于2025-09-23 15:21:21