Iterative Regularization Model for Image Denoising Based on Dual Norms

2012 ◽  
Vol 182-183 ◽  
pp. 1245-1249
Author(s):  
Guan Nan Chen ◽  
Dan Er Xu ◽  
Rong Chen ◽  
Zu Fang Huang ◽  
Zhong Jian Teng
Keyword(s):  
Image Denoising ◽  
Iterative Procedure ◽  
Convergence Property ◽  
Energy Functional ◽  
Image Features ◽  
Experimental Results ◽  
Iterative Regularization ◽  
Model Based ◽  
Oscillating Patterns

Image denoising algorithm based on gradient dependent energy functional often compromised the image features like textures or certain details. This paper proposes an iterative regularization model based on Dual Norms for image denoising. By using iterative regularization model, the oscillating patterns of texture and detail are added back to fit and compute the original Dual Norms model, and the iterative behavior avoids overfull smoothing while denoising the features of textures and details to a certain extent. In addition, the iterative procedure is proposed in this paper, and the proposed algorithm also be proved the convergence property. Experimental results show that the proposed method can achieve a batter result in preserving not only the features of textures for image denoising but also the details for image.

scholarly journals Generalized analytical model based on harmonic coupling for hybrid plasmonic modes: comparison with numerical and experimental results

2015 ◽  
Vol 23 (21) ◽  
pp. 27376 ◽  
Author(s):  
Mitradeep Sarkar ◽  
Jean-François Bryche ◽  
Julien Moreau ◽  
Mondher Besbes ◽  
Grégory Barbillon ◽  
...  
Keyword(s):  
Analytical Model ◽  
Experimental Results ◽  
Model Based

scholarly journals An Improved Image Denoising Model Based on Nonlocal Means Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yan Jin ◽  
Wenyu Jiang ◽  
Jianlong Shao ◽  
Jin Lu
Keyword(s):  
Image Denoising ◽  
Natural Images ◽  
Nonlocal Means ◽  
New Model ◽  
Model Based ◽  
Better Than

The nonlocal means filter plays an important role in image denoising. We propose in this paper an image denoising model which is a suitable improvement of the nonlocal means filter. We compare this model with the nonlocal means filter, both theoretically and experimentally. Experiment results show that this new model provides good results for image denoising. Particularly, it is better than the nonlocal means filter when we consider the denoising for natural images with high textures.

scholarly journals Research of Personalized Recommendation Technology Based on Knowledge Graphs

2021 ◽  
Vol 11 (15) ◽  
pp. 7104
Author(s):  
Xu Yang ◽  
Ziyi Huan ◽  
Yisong Zhai ◽  
Ting Lin
Keyword(s):  
Neural Network ◽  
Hot Spot ◽  
Experimental Results ◽  
Graph Representation ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Learning Technology ◽  
Data Set ◽  
Model Based ◽  
Knowledge Graphs

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs(KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

An Analysis of Conventional and Self-Aligned four Terminal Resistor Structures for The Determination of The Contact Resistivity from End Resistance Measurements

1986 ◽  
Vol 71 ◽  
Author(s):  
I. Suni ◽  
M. Finetti ◽  
K. Grahn
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Computer Model ◽  
Contact Resistivity ◽  
Experimental Results ◽  
The Finite Element Method ◽  
Model Based ◽  
A Value ◽  
And Diffusion

AbstractA computer model based on the finite element method has been applied to evaluate the effect of the parasitic area between contact and diffusion edges on end resistance measurements in four terminal Kelvin resistor structures. The model is then applied to Al/Ti/n+ Si contacts and a value of contact resistivity of Qc = 1.8×10−7.Ωcm2 is derived. For comparison, the use of a self-aligned structure to avoid parasitic effects is presented and the first experimental results obtained on Al/Ti/n+Si and Al/CoSi2/n+Si contacts are shown and discussed.

scholarly journals Person Reidentification Model Based on Multiattention Modules and Multiscale Residuals

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yongyi Li ◽  
Shiqi Wang ◽  
Shuang Dong ◽  
Xueling Lv ◽  
Changzhi Lv ◽  
...  
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Original Network ◽  
Fine Grained ◽  
Backbone Network ◽  
Model Based ◽  
Local Branch ◽  
Feature Expression ◽  
Global And Local

At present, person reidentification based on attention mechanism has attracted many scholars’ interests. Although attention module can improve the representation ability and reidentification accuracy of Re-ID model to a certain extent, it depends on the coupling of attention module and original network. In this paper, a person reidentification model that combines multiple attentions and multiscale residuals is proposed. The model introduces combined attention fusion module and multiscale residual fusion module in the backbone network ResNet 50 to enhance the feature flow between residual blocks and better fuse multiscale features. Furthermore, a global branch and a local branch are designed and applied to enhance the channel aggregation and position perception ability of the network by utilizing the dual ensemble attention module, as along as the fine-grained feature expression is obtained by using multiproportion block and reorganization. Thus, the global and local features are enhanced. The experimental results on Market-1501 dataset and DukeMTMC-reID dataset show that the indexes of the presented model, especially Rank-1 accuracy, reach 96.20% and 89.59%, respectively, which can be considered as a progress in Re-ID.

The Onset of Whirl Instability in Journal Bearings of Various Bore Shapes and Groove Sizes

1983 ◽  
Vol 105 (3) ◽  
pp. 342-352 ◽  
Author(s):  
M. Akko¨k ◽  
C. M. McC. Ettles
Keyword(s):  
Beneficial Effect ◽  
Load Capacity ◽  
Journal Bearings ◽  
Experimental Results ◽  
General Validity ◽  
Model Based ◽  
Linearized Model ◽  
Varying Viscosity

Experimental results are given for load capacity and whirl onset in journal bearings of circular, elliptical and offset halves bore shape. The general validity of the linearized model for predicting whirl is confirmed experimentally. Deviations between experimental results and the model, based on an isoviscous film, are attributed to the varying viscosity that occurs in practice, and to unavoidable excitation that gives rise to premature whirl. It is shown that increasing groove size has a destabilizing effect that can more than cancel the beneficial effect of preloading. This result is particularly relevant to the design of journal bearings in turbomachinery.

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