Weibo Wei

Qingdao University

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Zhenkuan Pan

Qingdao University

Guodong Wang

Qingdao University

Jinming Duan

University of Birmingham

Danfeng Hong

Aerospace Information Research Institute

Pan Zhen-kuan

Qingdao University

Baoxiang Huang

Qingdao University

Cuiping Wang

Xiamen University

Jintao Song

Shandong Institute of Business and Technology

Junli Zhao

Shanghai University of Medicine and Health Sciences

Huizhu Pan

Curtin University

Cooperative Institutions

Qingdao University

Qingdao University of Science and Technology

Ocean University of China

Chinese Academy of Sciences

Beijing Institute of Technology

Shanghai Jiao Tong University

Shandong University of Science and Technology

Tsinghua University

Liaocheng University

Northwestern Polytechnical University

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The Split Bregman Method for Image Diffusion on Implicit Surfaces

Zengfang Zhao Weibo Wei Zhenkuan Pan Cuiping Wang Shen Hu

Image diffusion on implicit surfaces is solved by the concepts of intrinsic gradient and intrinsic divergence along with the expression of implicit surfaces using zero level set of a continuous signed distance function. But the finite difference scheme of the relevant gradient descent equations is complex and its computation efficiency is low. The Split Bregman algorithm for the nonlinear variational image diffusion on implicit surfaces is designed by introducing auxiliary variables and Bregman iterative parameters, which transforms the previous energy functional minimization problem to solving some simple Poisson equations and some soft threshold formulas. The generalized TV model, PM model and Char bonnier model are implemented as examples to validate the proposed algorithm in image denoising and in painting on implicit surfaces and demonstrate its simplicity and efficiency.

Bregman divergence

Energy functional

Divergence (linguistics)

10.1109/isip.2010.137

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Anisotropic Variational Models for Image Denoising Based on Directional Hessian

Journal of Mathematical Imaging and Vision (2022)

Guangyu Yang Weibo Wei Zhenkuan Pan

Hessian matrix

Smoothness

10.1007/s10851-022-01122-x

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Citations (7)

Efficient L1-based nonlocal total variational model of Retinex for image restoration

Journal of Electronic Imaging (2018)

Guojia Hou Huizhu Pan Baoxiang Huang Guodong Wang Weibo Wei

Characteristics of an image, such as smoothness, edge, and texture, can be better preserved using the nonlocal differential operator in image processing. We establish an L1-based nonlocal total variational (NLTVL1) model based on Retinex theory that can be solved by a fast computational algorithm via the alternating direction method of multipliers. Experiential results demonstrate that our NLTVL1 method has a good performance on enhancing contrast, eliminating the influence of nonuniform illumination, and suppressing noise. Furthermore, compared with previous works, including traditional Retinex methods and variational Retinex methods, our proposed approach achieves superior performance on edge and texture preservation and needs fewer iterations on recovering the reflectance image, which is illustrated by examples and statistics.

Color Constancy

Smoothness

10.1117/1.jei.27.5.051207

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Citations (8)

Deep-Sea Debris Identification Using Deep Convolutional Neural Networks

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2021)

Bing Xue Baoxiang Huang Ge Chen Haitao Li Weibo Wei

Deep-sea debris is a globally growing problem that negatively impacting biological and chemical ecosystems.More seriously, the debris is likely to persist in the deep sea for long periods.Fortunately, with the help of the debris detection system the submersibles can clean up the debris.An excellent classifier is critical to the debris detection system.Therefore the objective of this study is to determine whether deep convolutional neural networks can distinguish the differences of debris and natural deep-sea environment, so as to effectively achieve deep-sea debris identification.First, a real deep-sea debris images (DDI) dataset is constructed for further classification research based on an online deep-sea debris database owned by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC).Second, the hybrid Shuffle-Xception network is constructed to classify the deep-sea image as metal, glass, plastic, rubber, fishing net &rope, natural debris, and cloth.Furthermore, five common convolutional neural networks (CNNs) frameworks are also employed to implement the classification process.Finally, the identification experiments are carried out to validate the performance of the proposed methodology.The results demonstrate that the proposed method is superior to the state-of-art CNN methods and has the potential for deep-sea debris identification.

Marine debris

Identification

10.1109/jstars.2021.3107853

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Citations (25)

New dual method for elastica regularization

PLoS ONE (2022)

Jintao Song Huizhu Pan Jieyu Ding Weibo Wei Zhenkuan Pan

The Euler’s elastica energy regularizer has been widely used in image processing and computer vision tasks. However, finding a fast and simple solver for the term remains challenging. In this paper, we propose a new dual method to simplify the solution. Classical fast solutions transform the complex optimization problem into simpler subproblems, but introduce many parameters and split operators in the process. Hence, we propose a new dual algorithm to maintain the constraint exactly, while using only one dual parameter to transform the problem into its alternate optimization form. The proposed dual method can be easily applied to level-set-based segmentation models that contain the Euler’s elastic term. Lastly, we demonstrate the performance of the proposed method on both synthetic and real images in tasks image processing tasks, i.e. denoising, inpainting, and segmentation, as well as compare to the Augmented Lagrangian method (ALM) on the aforementioned tasks.

Augmented Lagrangian method

Inpainting

Regularization

Solver

Energy functional

10.1371/journal.pone.0261195

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Bus Travel Time Prediction Based on Multi-Source Data Fusion

Lijun Zhu Weibo Wei Yang Xia Liping Fu

Advocating public transport travel can reduce carbon emissions and pollutant emissions, alleviating the problem of global warming. Accurate bus travel time prediction can improve the level of public transportation services and contribute to the improvement of intelligent transportation systems. Unfortunately, there are many complex factors that affect the accuracy of bus travel time prediction, which greatly affects the accuracy of the prediction. In addition, weather and travel dates have a greater impact on road congestion. Therefore, this paper proposes a bus travel time prediction method based on multi-source data fusion and captures external influencing factors such as weather and travel date. Learning spatial-temporal features between trajectory data using CNN and LSTM. The experimental results show that the model based on multi-source data fusion proposed in this paper can provide higher prediction accuracy compared with the model based on single-source data.

Sensor Fusion

Predictive modelling

Data source

10.1145/3565291.3565306

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Semi‐supervised learning dehazing algorithm based on the OSV model

IET Image Processing (2022)

Lijun Zhu Weibo Wei Zhenkuan Pan Lianshun Ji Jintao Song

Abstract Despite the great progress that has been made in the task of single image dehazing, the results of the existing models in restoring image edge and texture information are still challenging. Besides, most dehazing models are trained on synthetic data, resulting in poor generalization ability to real‐world images. To address the aforementioned problems, a semi‐supervised learning dehazing method based on the decomposition model of Osher, Solé, and Vese(The OSV model) is presented. Specifically, the OSV model is first applied to decompose the hazy image into the structure layer and texture layer, save the texture layer and dehaze for the structure layer to restore images with sharper texture and edge. Furthermore, the network adopts a semi‐supervised learning algorithm based on generative adversarial networks (GAN) to generalize better to real‐world images, which includes two branches: supervised learning and unsupervised learning. Extensive experiments indicate that the proposed method preserves the texture and edge information of images more accurately while dehazing better, and performs favourably against the advanced dehazing algorithms on both synthetic outdoor datasets and real‐world hazy images.

10.1049/ipr2.12679

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A Fast Robustness Palmprint Recognition Algorithm

Lecture notes in computer science (2014)

Danfeng Hong Xin Wu Zhenkuan Pan Jian Su Weibo Wei

Robustness

Curvelet

10.1007/978-3-319-12484-1_35

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Citations (1)

Dual Method for Small Displacement Optic Flow Computation

Jisuanji gongcheng (2010)

YU Jing-jing Pan Zhen-kuan Weibo Wei

Dual method is presented for small optic flow computation based on variational models.Robust estimation form of L1 norm is used in data terms and the regularized terms use total variation of optic flows with edge preserving property.The dual variables for total variation terms avoid using small regularized parameters in classic total variation terms and get good results.This method can be extended to computation of large displacement optical flow and higher order models.

Optical Flow

Variation (astronomy)

Source

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PottsNN: A Variational Neural Network Based on Potts Model for Image Segmentation

Lecture notes in computer science (2024)

Ye‐Ran Wang Zhirong Zhong Junli Zhao S. Gong Zhenkuan Pan

10.1007/978-981-97-8795-1_10

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