- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Quaternion-based weighted nuclear norm minimization for color image denoising
摘要： The quaternion method plays an important role in color image processing, because it represents the color image as a whole rather than as a separate color space component, thus naturally handling the coupling among color channels. The weighted nuclear norm minimization (WNNM) scheme assigns different weights to different singular values, leading to more reasonable image representation method. In this paper, we propose a novel quaternion weighted nuclear norm minimization (QWNNM) model and algorithm under the low rank sparse framework. The proposed model represents the color image as a low rank quaternion matrix, where quaternion singular value decomposition can be calculated by its equivalent complex matrix. We solve the QWNNM by adaptively assigning different singular values with different weights. Color image denoising is implemented by QWNNM based on non-local similarity priors. In this new color space, the inherent color structure can be well preserved during image reconstruction. For high noise levels, we apply a Gaussian lowpass filter (LPF) to the noisy image as a preprocessing before QWNNM, which reduces the iteration numbers and improves the denoised results. The experimental results clearly show that the proposed method outperforms K-SVD, QKSVD and WNNM in terms of both quantitative criteria and visual perceptual.
关键词： Quaternion singular value decomposition,Non-local similarity priors,Quaternion weighted nuclear norm minimization,Low rank,Color image denoising
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - An Iterative Adaptive Reweighted Norm Minimization Sparsity Autofocus Algorithm via Bayesian Recovery for Array SAR Imaging
摘要： The influence of phase error in echo signal is rarely considered or corrected by most classical compressed sensing (CS) algorithms, and reduces the quality of imaging results. In order to improve the quality of array synthetic aperture radar (ASAR) imaging, a new CS algorithm called Iterative Adaptive Reweighted Norm Minimization Sparsity Autofocus algorithm via Bayesian Recovery (IARNSABR) was proposed in this paper. Based on the principle of Bayesian Recovery, the iterative adaptive reweighted norm minimization method has been used in the process of reconstruction. The theoretical model and the process of imaging of IARNSABR has been established. And the proposed algorithm can correct the influence of phase error more effectively, and has stronger ability of eliminating the false targets. Through simulation and experiment results, IARNSABR can achieve higher quality imaging than SAFBRIM.
关键词： Compressed Sensing,Sparse autofocus,Iterative Reweighted Adaptive Norm Minimization,ASAR
更新于2025-09-23 15:22:29
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[ACM Press the 2018 International Conference - Porto, Portugal (2018.07.15-2018.07.17)] Proceedings of the 2018 International Conference on Mathematics and Statistics - ICoMS 2018 - Singular Value Decomposition and its Applications in Image Processing
摘要： The Singular Value Decomposition (SVD) is a highlight of linear algebra and has a wide range application in computer vision, statistics and machine learning. This paper reviews the main theorem of SVD and illustrates some applications of SVD in image processing. More specifically, we focus on image compression and matrix completion. The former is to convert the original full-rank pixel matrix to a well-approximated low-rank matrix and thus dramatically save the space, the latter is to recover a pixel matrix with a large number of missing entries by using nuclear norm minimization, in which some singular value thresholding algorithm will be used. For both applications, we conduct numerical experiments to show the performance and point out some possible improvements in the future.
关键词： Nuclear norm minimization,Matrix completion,Image compression,SVD
更新于2025-09-23 15:22:29
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[IEEE TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) - Kochi, India (2019.10.17-2019.10.20)] TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) - Use of Novel Hybrid Plasmonic Nanoparticle Complexes to Increase the Efficiency of Thin-film Solar Cells
摘要： To utilize the synergy between computed tomography (CT) and magnetic resonance imaging (MRI) data sets from an object at the same time, an edge-guided dual-modality image reconstruction approach is proposed. The key is to establish a knowledge-based connection between these two data sets for the tight fusion of different imaging modalities. Our scheme consists of four inter-related elements: 1) segmentation; 2) initial guess generation; 3) CT image reconstruction; and 4) MRI image reconstruction. Our experiments show that, aided by the image obtained from one imaging modality, even with highly under-sampled data, we can better reconstruct the image of the other modality. This approach can be potentially useful for a simultaneous CT-MRI system.
关键词： l1-norm minimization,image reconstruction,CT-MRI system,multi-modality imaging
更新于2025-09-23 15:19:57
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Effect of Secondary Emission Yield and Initial Charge of Dielectric Material on Multipactor in Parallel-Plate Dielectric-Loaded Waveguide
摘要： We present a theoretical analysis and comparison of the effect of (cid:2)1 versus (cid:2)2 regularization for the resolution of ill-posed linear inverse and/or compressed sensing problems. Our formulation covers the most general setting where the solution is speci?ed as the minimizer of a convex cost functional. We derive a series of representer theorems that give the generic form of the solution depending on the type of regularization. We start with the analysis of the problem in ?nite dimensions and then extend our results to the in?nite-dimensional spaces (cid:2)2(Z) and (cid:2)1(Z). We also consider the use of linear transformations in the form of dictionaries or regularization operators. In particular, we show that the (cid:2)2 solution is forced to live in a prede?ned subspace that is intrinsically smooth and tied to the measurement operator. The (cid:2)1 solution, on the other hand, is formed by adaptively selecting a subset of atoms in a dictionary that is speci?ed by the regularization operator. Beside the proof that (cid:2)1 solutions are intrinsically sparse, the main outcome of our investigation is that the use of (cid:2)1 regularization is much more favorable for injecting prior knowledge: it results in a functional form that is independent of the system matrix, while this is not so in the (cid:2)2 scenario.
关键词： linear inverse problems,compressed sensing,regularization,Sparsity,total variation,(cid:2)1-norm minimization
更新于2025-09-23 15:19:57
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Rotational Invariant Discriminant Subspace Learning For Image Classification
摘要： A novel discriminant analysis technique for feature extraction, referred to as Robust Discriminant Subspace (RDS) with L2,p+s-Norm Distance Maximization-Minimization (maxmin) is posed. In its objective, the within-class and between-class distances are measured by L2,p-norm and L2,s-norm, respectively, such that it is robust and rotational invariant. An efficient iterative algorithm is designed to solve the resulted objective, which is non-greedy. We also conduct some insightful analysis on the convergence of the proposed algorithm. Theoretical insights and effectiveness of our RDS are further supported by promising experimental results on several images databases.
关键词： p-norm distance maximization,s-norm minimization,L2,robust discriminant analysis
更新于2025-09-11 14:15:04