- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Adaptive SVD Domain-Based White Gaussian Noise Level Estimation in Images
摘要： Noise level estimation is a challenging area of digital image processing with a variety of applications, including image enhancement, image segmentation, and feature extraction. In this paper, an adaptive estimation of additive white Gaussian noise level based on the singular value decomposition (SVD) of images is proposed. The proposed algorithm aims to improve the performance of noise level estimation in the SVD domain at low noise levels. An initial noise level estimate is used to adjust the parameters of the algorithm in order to increase the accuracy of noise level estimation. The proposed algorithm exhibits the ability to adapt the number of considered singular values and to accordingly adjust the slope of a linear function that describes how the average value of the singular value tail varies with noise levels. Although, for each image, the proposed algorithm performs the noise level estimation twice in two distinct stages, the singular value decompositions are only performed in the first stage of the algorithm. The experimental results demonstrate that the proposed algorithm improves the noise level estimation at low noise levels without a significant increase in computational complexity. At noise level σ = 15, the improvements in the mean square level are about 39% at the expense of slightly higher additional computational time.
关键词： artificial neural networks,singular value decomposition,image analysis,noise level estimation,Digital images,AWGN,least square methods
更新于2025-09-23 15:22:29
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Optimal Hexagonal Constellations Based on A Two-Dimensional Signal Space for Peak-Limited Intensity-Modulated Channels
摘要： For high-rate peak-limited intensity-modulated optical systems, the two-dimensional time-disjoint signal space (TDSS) is proposed. Based on the TDSS, the optimal hexagonal constellations (OHCs) are proposed analytically under a peak optical power constraint. Both analytical and simulation results show our proposed OHCs based on TDSS have asymptotical peak optical power gain of 0.753 dB over the baseline schemes at no extra bandwidth cost.
关键词： two-dimensional,peak-limited,IM/DD,signal space,Hexagonal lattice,AWGN
更新于2025-09-23 15:22:29
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Synthesis of Uniform Alkane-Filled Capsules with a High Under-Cooling Performance and Their Real-Time Optical Properties
摘要： In this paper, the conditional triplet-wise error probability is proposed to improve Gallager’s first bounds based on the general framework of parameterized Gallager’s first bounding technique (GFBT) of binary linear block codes over additive white Gaussian noise (AWGN) channels, which can alleviate the repeated accumulations caused by the use of the pair-wise error probability. Within the recently proposed bounding framework based on nested Gallager regions, three well-known upper bounds, namely, the sphere bound (SB) of Herzberg and Poltyrev, the tangential bound (TB) of Berlekamp, and the tangential-sphere bound (TSB) of Poltyrev, are visited. Within the proposed bounding framework based on conditional triplet-wise error probability, the three well-known bounds can be improved by exploring more detailed geometrical structure of the code when upper bounding the error probabilities. Numerical results show that the proposed bounding framework is useful since the proposed bounds can even improve the TSB, which is considered as one of the tightest upper bounds.
关键词： Parameterized GFBT,Maximum-likelihood (ML) decoding,Triplet-wise error probability,Gallager’s first bounding technique (GFBT),Additive white Gaussian noise (AWGN) channel
更新于2025-09-11 14:15:04
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Hyperspectral Unmixing with Bandwise Generalized Bilinear Model
摘要： Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.
关键词： alternative direction method of multipliers (ADMM),bandwise generalized bilinear model (BGBM),hyperspectral images (HSIs),additive white Gaussian noise (AWGN),mixed noise
更新于2025-09-09 09:28:46