842 research outputs found
Multiplicative Noise Removal: Nonlocal Low-Rank Model and It\u27s Proximal Alternating Reweighted Minimization Algorithm
The goal of this paper is to develop a novel numerical method for efficient multiplicative noise removal. The nonlocal self-similarity of natural images implies that the matrices formed by their nonlocal similar patches are low-rank. By exploiting this low-rank prior with application to multiplicative noise removal, we propose a nonlocal low-rank model for this task and develop a proximal alternating reweighted minimization (PARM) algorithm to solve the optimization problem resulting from the model. Specifically, we utilize a generalized nonconvex surrogate of the rank function to regularize the patch matrices and develop a new nonlocal low-rank model, which is a nonconvex non-smooth optimization problem having a patchwise data fidelity and a generalized nonlocal low-rank regularization term. To solve this optimization problem, we propose the PARM algorithm, which has a proximal alternating scheme with a reweighted approximation of its subproblem. A theoretical analysis of the proposed PARM algorithm is conducted to guarantee its global convergence to a critical point. Numerical experiments demonstrate that the proposed method for multiplicative noise removal significantly outperforms existing methods, such as the benchmark SAR-BM3D method, in terms of the visual quality of the denoised images, and of the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) values
Multiplicative Noise Removal: Nonlocal Low-Rank Model and Its Proximal Alternating Reweighted Minimization Algorithm
The goal of this paper is to develop a novel numerical method for efficient
multiplicative noise removal. The nonlocal self-similarity of natural images
implies that the matrices formed by their nonlocal similar patches are
low-rank. By exploiting this low-rank prior with application to multiplicative
noise removal, we propose a nonlocal low-rank model for this task and develop a
proximal alternating reweighted minimization (PARM) algorithm to solve the
optimization problem resulting from the model. Specifically, we utilize a
generalized nonconvex surrogate of the rank function to regularize the patch
matrices and develop a new nonlocal low-rank model, which is a nonconvex
nonsmooth optimization problem having a patchwise data fidelity and a
generalized nonlocal low-rank regularization term. To solve this optimization
problem, we propose the PARM algorithm, which has a proximal alternating scheme
with a reweighted approximation of its subproblem. A theoretical analysis of
the proposed PARM algorithm is conducted to guarantee its global convergence to
a critical point. Numerical experiments demonstrate that the proposed method
for multiplicative noise removal significantly outperforms existing methods
such as the benchmark SAR-BM3D method in terms of the visual quality of the
denoised images, and the PSNR (the peak-signal-to-noise ratio) and SSIM (the
structural similarity index measure) values
Morph-specific differences in life history traits between the winged and wingless morphs of the aphid, Sitobion avenae (Fabricius) (Hemiptera: Aphididae)
Life history traits were evaluated in the wing polyphenic aphid, Sitobion avenae (Fabricius), by rearing the winged and wingless morphs under the laboratory conditions. Winged morph with large thoraces exhibited a significantly greater morphological investment in flight apparatus than wingless morph with small thoraces. Compared to the winged morph, the wingless morph produced significantlymore nymphs and exhibited significantly faster nymph development rates. In addition, the age at which reproduction first occurred for the winged morph was significantly delayed, and higher mortality was recorded.The results suggest that the fitness differences associated with wingsmay be related to nymph development, adult fecundity, and mortality. Based on these results, the trends and exceptions of life history traits for the wing polyphenic insects are discussed
Numerical Analysis of Partial Abrasion of the Straddle-type Monorail Vehicle running Tyre
The finite element model of the running tyre and the pre-stressed concrete (PC) track beam are created in the study. The wheel-rail contact status under the conditions such as acceleration or braking, lateral deviation, and roll is analysed. The wear law of the running tyre under the operating condition of driving on winding roads is discussed. The results show that the running tyre will unevenly wear when driving on the winding road; the smaller curve radius and the faster speed result in heavier and more uneven wear. There is a larger slip between the running tyre on the inner side of the curve and the rail surface, and this tyre has more uneven wear than the running tyre on the outer side of the curve. The research findings provide a theoretical basis for solving the problem of reducing the uneven wear of the running tyre
A Generalized Cluster-Free NOMA Framework Towards Next-Generation Multiple Access
A generalized downlink multi-antenna non-orthogonal multiple access (NOMA)
transmission framework is proposed with the novel concept of cluster-free
successive interference cancellation (SIC). In contrast to conventional NOMA
approaches, where SIC is successively carried out within the same cluster, the
key idea is that the SIC can be flexibly implemented between any arbitrary
users to achieve efficient interference elimination. Based on the proposed
framework, a sum rate maximization problem is formulated for jointly optimizing
the transmit beamforming and the SIC operations between users, subject to the
SIC decoding conditions and users' minimal data rate requirements. To tackle
this highly-coupled mixed-integer nonlinear programming problem, an alternating
direction method of multipliers-successive convex approximation (ADMM-SCA)
algorithm is developed. The original problem is first reformulated into a
tractable biconvex augmented Lagrangian (AL) problem by handling the non-convex
terms via SCA. Then, this AL problem is decomposed into two subproblems that
are iteratively solved by the ADMM to obtain the stationary solution. Moreover,
to reduce the computational complexity and alleviate the parameter
initialization sensitivity of ADMM-SCA, a Matching-SCA algorithm is proposed.
The intractable binary SIC operations are solved through an extended
many-to-many matching, which is jointly combined with an SCA process to
optimize the transmit beamforming. The proposed Matching-SCA can converge to an
enhanced exchange-stable matching that guarantees the local optimality.
Numerical results demonstrate that: i) the proposed Matching-SCA algorithm
achieves comparable performance and a faster convergence compared to ADMM-SCA;
ii) the proposed generalized framework realizes scenario-adaptive
communications and outperforms traditional multi-antenna NOMA approaches in
various communication regimes.Comment: 30 pages, 9 figures, submitted to IEEE TW
Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation
Semi-supervised semantic segmentation (SSS) has recently gained increasing
research interest as it can reduce the requirement for large-scale
fully-annotated training data. The current methods often suffer from the
confirmation bias from the pseudo-labelling process, which can be alleviated by
the co-training framework. The current co-training-based SSS methods rely on
hand-crafted perturbations to prevent the different sub-nets from collapsing
into each other, but these artificial perturbations cannot lead to the optimal
solution. In this work, we propose a new conflict-based cross-view consistency
(CCVC) method based on a two-branch co-training framework which aims at
enforcing the two sub-nets to learn informative features from irrelevant views.
In particular, we first propose a new cross-view consistency (CVC) strategy
that encourages the two sub-nets to learn distinct features from the same input
by introducing a feature discrepancy loss, while these distinct features are
expected to generate consistent prediction scores of the input. The CVC
strategy helps to prevent the two sub-nets from stepping into the collapse. In
addition, we further propose a conflict-based pseudo-labelling (CPL) method to
guarantee the model will learn more useful information from conflicting
predictions, which will lead to a stable training process. We validate our new
CCVC approach on the SSS benchmark datasets where our method achieves new
state-of-the-art performance. Our code is available at
https://github.com/xiaoyao3302/CCVC.Comment: accepted by CVPR202
Consensus measure with multi-stage fluctuation utility based on China’s urban demolition negotiation
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Utility functions are often used to reflect decision makers' (DMs') preferences. They have the following two merits: one refers to the representation of the DM's utility (satisfaction) level, the other one to the measuring of the consensus level in a negotiation process. Taking the background of China's urban house demolition, a new kind of consensus model is established by using di erent types of multi-stage fluctuation utility functions, such as concave, convex, S-shaped, reversed S-shaped, reversed U-shaped as well as their combinations, to reveal
negotiators' dynamic physiological preferences and consensus level. Moreover, the eff ects of
budget and the individual compensation tolerance on the consensus level are also discussed
with previous research, the proposed model takes both the negotiation cost and DM's consideration, and most importantly, it is computational less complex
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