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检索条件"任意字段=Conference on Nonlinear Image Processing XI"

共 188 条记录，以下是1-10 订阅

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A Parametric Non-Negative Coupled Canonical Polyadic Decomposition Algorithm for Hyperspectral Super-Resolution

A Parametric Non-Negative Coupled Canonical Polyadic Decompo...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Liu, xi-Yuan Gong, xiao-Feng Wang, Lei Feng, Wei Lin, Qiu-Hua School of Information and Communication Engineering Dalian University of Technology China

ISBN: (纸本)9798350368741

Recently, coupled tensor decomposition has been widely used in data fusion of a hyperspectral image (HSI) and a multispectral image (MSI) for hyperspectral super-resolution (HSR). However, exsiting works often ignore the inherent non-negative (NN) property of the image data, or impose the NN constraint via hard-thresholding which may interfere with the optimization procedure and cause the method to be sub-optimal. As such, we propose a novel NN coupled canonical polyadic decomposition (NN-C-CPD) algorithm, which makes use of the parametric method and nonlinear least squares (NLS) framework to impose the NN constraint into the C-CPD computation. More exactly, the NN constraint is converted into the squared relationship between the NN entries of the factor matrices and a set of latent parameters. Based on the chain rule for deriving the derivatives, the key entities such as gradient and Jacobian with regards to the latent parameters can be derived, thus the NN constraint is naturally integrated without interfering with the optimization procedure. Experimental results are provided to demonstrate the performance of the proposed NN-C-CPD algorithm in HSR applications. © 2025 IEEE.

关键词： Coupled canonical polyadic decomposition hyperspectral super-resolution non-negative tensor

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Evaluating multi-modal image matching algorithms for remote sensing applications 6

Evaluating multi-modal image matching algorithms for remote ...

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6th International conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Liu, Zhanquan Han, Yilong Fu, Mingsheng Song, Baiwan Gong, Danchao Ming, Yanfang Sun, Lin College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao266590 China Shandong Province Research Center of Engineering and Technology for GPS RS and GIS Qingdao266590 China Star-Rising Technologies Co. Ltd. Qingdao266590 China Chinese Academy of Surveying and Mapping Beijing100036 China Shandong GEO-Surveying & Mapping Institute Jinan250000 China State Key Laboratory of Geo-Information Engineering Xi’an710054 China Xi’an Institute of Surveying and Mapping Xi’an710054 China

ISBN: (纸本)9781510688018

image matching is a fundamental task in image processing, particularly in photogrammetry and remote sensing. Conventional image matching methods often struggle with multi-modal images due to significant geometric distortions and nonlinear radiometric effects. To address these challenges, researchers have developed a variety of algorithms specifically tailored for multi-modal data. Despite this, there is a notable lack of comprehensive theoretical summaries of multi-modal image matching. This paper aims to bridge this gap by discussing the complexities of matching multi-modal images and providing a succinct overview of existing methods. We evaluate three Conventional algorithms—OS-SIFT, RIFT, and POS-GIFT—and two deep learning methods—SemLA and SuperPoint+SuperGlue. These methods are assessed using six types of multi-modal remote sensing datasets: Optical-Optical, Optical-Infrared, Optical-Depth, Optical-Map, Optical-Synthetic Aperture Radar (SAR), and Day-Night. The results indicate that the POS-GIFT algorithm outperforms others, achieving an average of 1217 correct matches with a success rate of 96.70%. This paper concludes by discussing the challenges of multi-modal image matching and suggesting future research directions, serving as a valuable resource for researchers and practitioners in remote sensing. © 2025 SPIE.

关键词： image matching

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A Parametric Non-Negative Coupled Canonical Polyadic Decomposition Algorithm for Hyperspectral Super-Resolution

A Parametric Non-Negative Coupled Canonical Polyadic Decompo...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： xi-Yuan Liu xiao-Feng Gong Lei Wang Wei Feng Qiu-Hua Lin School of Information and Communication Engineering Dalian University of Technology China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Jacobian matrices Tensors Superresolution Noise Signal processing algorithms Matrix decomposition Matrix converters Speech processing Optimization Hyperspectral imaging

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Removal of mixed noise on color image processing by using fuzzy rules

Removal of mixed noise on color image processing by using fu...

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conference on nonlinear image processing xi

作者： Taguchi, A Hamada, T Musashi Inst Technol Dept Elect & Elect Engn Setagaya Ku Tokyo 158 Japan

ISBN: (纸本)0819435791

We have proposed fuzzy filters in order to remove additive non-impulsive noise (e.g,, Gussian noise) while preserving signal details. In this paper, we propose a novel fuzzy filter for removing mixed noise (i.e., Gaussian noise and impulse noise are mixed). Furthermore, we apply the proposed method to color image processing. In order to remove mixed noise efficiently, we set fuzzy rules by using multiple difference values between arbitrary two pixels in a filter window. We show tuning result of the proposed fuzzy filter and present some simulation results.

关键词： image filtering Optical filters Digital filtering Color image processing Fuzzy logic Gaussian filters nonlinear filtering Interference (communication) Electronic filtering Machine vision

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The construction of a statistical prediction lifting operator and its application

The construction of a statistical prediction lifting operato...

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IEEE International conference on image processing

作者： Li, HL Liu, GZ Li, YL Hou, XS Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

ISBN: (纸本)0780376226

A new method of nonseparable nonlinear wavelet decomposition is proposed, which is suited for the task of image compression, especially for lossless coding applications. It is based on a certain statistical operator that is defined here according to the markov random field theory. In contrast to the previous nonlinear predictors such as the median or morphological operators, this statistical operator can sufficiently take advantage of the statistical correlation between neighboring pixels. It can be used to realize integer-valued wavelet transforms, which can avoid quantization with the image detail signals being zero (or almost zero) in the smooth graylevel variation areas at a big probability. Numerical results show that the entropy of the coefficients in the transform domain obtained with this new method is smaller than that obtained with the other nonlinear transform methods.

关键词： image compression

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Enlargement method of digital images based on laplacian pyramid representation

Enlargement method of digital images based on laplacian pyra...

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conference on nonlinear image processing xi

作者： Taguchi, A Takahashi, Y Musashi Inst Technol Dept Elect & Elect Engn Tokyo 1588557 Japan

ISBN: (纸本)0819435791

We present novel enlarging methods of digital images based on Laplacian pyramid representation. First, "direct method", which is based on Laplacian pyramid representation, is proposed. The direct method is simple, however derive excellent enlargement results for only relative smooth regions of the image. Thus, we present the "hybrid method" which is integrated the direct method and the Greenspan's method which is also based on Laplacian pyramid representation and has excellent property for enraging edge/detail regions. The effectiveness of the hybrid method is shown through a lot of experimental results.

关键词： Direct methods Convolution Cameras Gaussian filters Chlorine image processing Dielectrophoresis Electronics engineering nonlinear image processing Osmium

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Vessel image Enhancement Method Using nonlinear Transformation Based on Guided Filtering

Vessel Image Enhancement Method Using Nonlinear Transformati...

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2021 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2021

作者： Qi, Tang Wen-Tian, Wang Ke-xin, Zhou Yi-Xuan, Sun Xi'An University of Posts Telecommunication Xi'an China

ISBN: (纸本)9781665429184

Aiming at the problems of blurred contrast and difficult diagnosis of vascular tissue in medical images, A nonlinear transform blood vessel enhancement method based on guided filtering is proposed. In this paper, a hybrid filter is obtained by fusing guided filtering and Laplacian filtering to complete the preprocessing of image vessel detail enhancement, then the green component is stretched by nonlinear mapping function in RGB color space, the red component and blue component are adjusted according to the stretching degree. Finally, the saturation component is nonlinear contrast stretched in HSV color space to achieve vascular image enhancement. The paper uses objective evaluation methods such as image DV-BV, information entropy and average gradient to compare the performance of existing image enhancement algorithms. The results show that the proposed method has excellent performance. © 2021 IEEE.

关键词： image enhancement

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A SINGLE-image SUPER-RESOLUTION METHOD VIA LOW-RANK MATRIX RECOVERY AND nonlinear MAPPINGS

A SINGLE-IMAGE SUPER-RESOLUTION METHOD VIA LOW-RANK MATRIX R...

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20th IEEE International conference on image processing (ICIP)

作者： Chen, xiaoxuan Qi, Chun Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian Peoples R China

ISBN: (纸本)9781479923410

This paper presents a novel method for single-image super-resolution (SR) reconstruction using the low-rank matrix recovery and nonlinear mappings. First, the low-rank matrix recovery is utilized to learn the underlying structures of subspaces spanned by the grouped patch features. Second, the low-rank components of low-resolution (LR) and high-resolution (HR) patch features are mapped onto high-dimensional spaces by nonlinear mappings respectively. Then the mapped high-dimensional vectors are projected onto a unified space, where the two manifolds constructed by LR and HR patches respectively have similar local geometry and the SR reconstruction is performed via neighboring embedding. The experimental results validate the effectiveness of our method and suggest that the proposed method outperforms other SR algorithms qualitatively and quantitatively.

关键词： Super-resolution low-rank matrix recovery nonlinear mapping neighbor embedding

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nonlinear PREDICTION OF MULTIDIMENSIONAL SIGNALS VIA DEEP REGRESSION WITH APPLICATIONS TO image CODING 44

NONLINEAR PREDICTION OF MULTIDIMENSIONAL SIGNALS VIA DEEP RE...

引用

44th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Zhang, xi Wu, xiaolin Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada

ISBN: (纸本)9781479981311

Deep convolutional neural networks (DCNN) have enjoyed great successes in many signal processing applications because they can learn complex, non-linear causal relationships from input to output. In this light, DCNNs are well suited for the task of sequential prediction of multidimensional signals, such as images, and have the potential of improving the performance of traditional linear predictors. In this research we investigate how far DCNNs can push the envelop in terms of prediction precision. We propose, in a case study, a two-stage deep regression DCNN framework for nonlinear prediction of two-dimensional image signals. In the first-stage regression, the proposed deep prediction network (PredNet) takes the causal context as input and emits a prediction of the present pixel. Three PredNets are trained with the regression objectives of minimizing l(1), l(2) and l(infinity) norms of prediction residuals, respectively. The second-stage regression combines the outputs of the three PredNets to generate an even more precise and robust prediction. The proposed deep regression model is applied to lossless predictive image coding, and it outperforms the state-of-the-art linear predictors by appreciable margin.

关键词： Deep regression nonlinear prediction lossless image coding

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nonlinear MULTI-SCALE CONTRAST ENHANCEMENT FOR CHEST RADIOGRAPH

NONLINEAR MULTI-SCALE CONTRAST ENHANCEMENT FOR CHEST RADIOGR...

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15th IEEE International conference on image processing (ICIP 2008)

作者： Mou, Xuanqin Zhang, Min Xi An Jiao Tong Univ Inst Image Proc & Pattern Recognit Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781424417650

Recently, extensive research and application on contrast enhancement of radiographs based on multi-scale decomposition of the images have validated its higher performance than regular techniques. However, to some extent, conventional multi-scale methods suffered from the introduction of visible artifacts. In this work, we present an algorithm for nonlinear chest radiograph contrast enhancement algorithm within the multi-scale decomposition architecture in spatial domain. In particular, one kind of nonlinear enhancement function is designed by exploiting local contrast information. The main contribution of this model is the local adaptive enhancement ability, which can avoid visible artifacts, while keeping the same detail enhancement ability. In the meantime, no excessive noise is amplified, comparing to conventional methods. Finally, an evaluation using a chest image is provided to demonstrate the effectiveness of the proposed algorithm.

关键词： Adaptive contrast enhancement chest radiography multi-scale

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