Article
Chemistry, Analytical
Xu He, Yong Yin
Summary: This study proposes a multi-scale architecture combining a contextual attention module and implicit diversified Markov random field loss to guide the generative network in restoring irregular and large masked image areas. Experimental results demonstrate that the proposed framework significantly outperforms the state-of-the-art approaches in both quantity and quality.
Article
Computer Science, Software Engineering
Jing Lian, Jiajun Zhang, Jizhao Liu, Zilong Dong, Huaikun Zhang
Summary: Recent advancements in image inpainting techniques have focused on generating realistic structure and texture features in missing regions. However, current methods often suffer from inconsistent contextual semantics and blurry textures. To address these issues, this study proposes a dual-feature encoder and multi-scale receptive fields to improve the consistency of contextual semantics and image details.
Article
Computer Science, Artificial Intelligence
Manjunath R. Hudagi, Shridevi Soma, Rajkumar L. Biradar
Summary: This paper proposes an effective hybrid image inpainting method that combines ALG, DCNN, KNN, and bi-harmonic function to achieve good results in image restoration.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Youngdoo Kim, Phong C. H. Nguyen, Hoon Kim, Hae-Jin Choi, Young Choi
Summary: Multi-morphology cellular structures have gained attention due to their ability to adjust geometric and mechanical properties. This study characterizes the deformation of these structures and proposes a deformation prediction method. The effects of design variables and neighboring unit cells on deformation were measured, and a prediction model considering neighboring effects was developed. Numerical studies validated the method, showing good agreement between optimized cellular structures and desired deformation.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Sukla Satapathy, Rajiv Ranjan Sahay
Summary: In this study, the inpainting of depth maps with missing data regions is addressed. Initially, missing depth values at random locations and due to overlaid text are inpainted, followed by a proposed approach for filling large holes in the input depth map utilizing superpixel division of the corresponding RGB image. The use of a non-local extension of the classical Gauss-Markov random field model for the completed depth map proves to be effective in estimating missing information based on self-similarities between non-local patches inside a superpixel based search window.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2021)
Article
Computer Science, Software Engineering
Hu Chen, Jia Li, Junjie Zhang, Yu Fu, Chenggang Yan, Dan Zeng
Summary: Due to system instability and atmospheric interference, hyperspectral images often lose information in areas with different shapes, degrading data quality and limiting subsequent tasks. To address this, we propose GLCSA-Net, a spectral adaptive network that reduces spectral redundancy and maintains texture consistency. It achieved better results than state-of-the-art methods in quantitative and qualitative assessments.
Article
Computer Science, Information Systems
Saurabh Agarwal, Ki-Hyun Jung
Summary: This paper proposes a robust image forensics technique based on multi-direction threshold (MDT) for detecting median filtering. The method derives an optimal thresholded array from difference arrays in multiple directions and applies the Markov process to fetch joint probability statistics. It achieves high detection accuracy on JPEG compressed images of various quality factors by utilizing both first and second-order difference arrays.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Xinru Shao, Hailiang Ye, Bing Yang, Feilong Cao
Summary: This study proposes a two-stream coupling network for image inpainting, which incorporates structure reconstruction and texture synthesis with bidirectional interaction mechanisms. Experimental results show the superiority of the proposed method in image inpainting.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jia Qin, Huihui Bai, Yao Zhao
Summary: Recently, multi-scale neural networks have shown promising improvements in image inpainting. However, most of them adopt a progressive approach that can propagate errors from lower scales to higher scales. In this regard, we propose a multi-level augmented inpainting network (MLA Net) that effectively addresses the inter- and intra-level context harmonization. The proposed network includes a pyramid reconstruction structure (PRS) to establish inter-level relationships and a spatial similarity based attention mechanism (SSA) to ensure intra-level local continuity. Experimental results demonstrate that MLA-Net outperforms state-of-the-art methods in terms of accuracy and visual quality.
PATTERN RECOGNITION
(2022)
Article
Engineering, Electrical & Electronic
Chaoqun Wang, Xuejin Chen, Shaobo Min, Jiaping Wang, Zheng-Jun Zha
Summary: The study proposes a structure-guided video inpainting approach to improve video inpainting results by enhancing temporal structure coherence. By completing edges in the missing regions and replenishing textures under the guidance of edges, the method achieves more realistic content synthesis. Motion flows are utilized to enhance temporal consistency during self-supervised training.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Wenwen Ye, Shengping Li
Summary: Flow direction algorithm (FDA) is a physics-based optimization algorithm for solving global optimization problems, lacking theoretical guarantees. This paper establishes a Markov process model to prove that FDA is globally convergent with probability 1. Additionally, an improved FDA algorithm (IFDA) is proposed to enhance exploration and exploitation abilities through random opposition-based learning and adaptive neighbor generation strategy. Extensive experiments and tests demonstrate the efficiency and effectiveness of the proposed algorithm on benchmark functions and a wireless sensor network coverage optimization problem.
Article
Computer Science, Information Systems
Ting Xu, Ting-Zhu Huang, Liang-Jian Deng, Xi-Le Zhao, Jin-Fan Hu
Summary: This study proposes a new exemplar-based image inpainting method that considers the filling order and local intensity smoothness. It prevents geometric structures from being destroyed and reconstructs textures well to achieve elegant-looking outputs.
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
(2022)
Article
Humanities, Multidisciplinary
V. Rakhi Mol, P. Uma Maheswari
Summary: Murals are artistic works applied directly on walls or other surfaces, representing various cultures and traditions. However, they are often degraded by natural causes, pollution, and human damage, requiring skilled artisans for restoration. To address these challenges, an efficient image restoration technique is needed to reconstruct both structure and texture of ancient murals.
Article
Astronomy & Astrophysics
Yuji Tanaka, Mamoru Ota, Yoshiya Kasahara
Summary: Two design methods were proposed to improve the accuracy of direction estimation when noise levels among electromagnetic field sensors are different. Model 2 improved the misalignment of the peak in the direction of arrival under different noise levels, regardless of whether the sensor was parallel or perpendicular to the external magnetic field.
Article
Computer Science, Information Systems
Ruisong Zhang, Weize Quan, Yong Zhang, Jue Wang, Dong-Ming Yan
Summary: This article introduces a new image inpainting network called W-Net, which addresses the issues of existing methods through texture spatial attention and structure channel excitation modules, and achieves superior performance.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Engineering, Electrical & Electronic
Alam Abbas Syed, Hassan Foroosh
Summary: This paper presents effective methods using spherical polar Fourier transform data for two different applications: 3D volumetric registration and machine learning classification network. The proposed method for registration offers unique and effective techniques, handling arbitrary large rotation angles and showing robustness. The modified classification network achieves robust classification results in processing spherical data.
Article
Engineering, Electrical & Electronic
Ruibo Fan, Mingli Jing, Jingang Shi, Lan Li, Zizhao Wang
Summary: In this study, a new low-rank sparse decomposition algorithm named TVRPCA+ is proposed for foreground-background separation. The algorithm combines spectral norm, structured sparse norm, and total variation regularization to suppress noise and obtain cleaner foregrounds. Experimental results demonstrate that TVRPCA+ achieves high performance in complex backgrounds and noise scenarios.
Article
Engineering, Electrical & Electronic
Omair Aldimashki, Ahmet Serbes
Summary: This paper proposes a coarse-to-fine FrFT-based algorithm for chirp-rate estimation of multi-component LFM signals, which achieves improved performance and a reduced signal-to-noise breakdown threshold by utilizing mathematical models for coarse estimation and a refined estimate-and-subtract strategy. Extensive simulation results demonstrate that the proposed algorithm performs very close to the Cramer-Rao lower bound, with the advantages of eliminating leakage effect, avoiding error propagation, and maintaining acceptable computational cost compared to other state-of-the-art methods.
Article
Engineering, Electrical & Electronic
Xinlei Shi, Xiaofei Zhang, Yuxin Sun, Yang Qian, Jinke Cao
Summary: In this paper, a low-complexity localization approach for multiple sources using two-dimensional discrete Fourier transform (2D-DFT) is proposed. The method computes the cross-covariance and utilizes phase offset method and total least square solution to obtain accurate position estimates.
Article
Engineering, Electrical & Electronic
Prabhanjan Mannari, Ratnasingham Tharmarasa, Thiagalingam Kirubarajan
Summary: This paper discusses the problem of extended target tracking for a single 2D extended target with a known convex polytope shape and dynamics. It proposes a framework based on the existing point multitarget tracking framework to address the challenges of uncertainty in shape and kinematics, as well as self-occlusion. The algorithm developed using this framework is capable of dynamically changing the number of parameters used to describe the shape and estimating the whole target shape even when different parts of the target are visible at different frames.
Article
Engineering, Electrical & Electronic
Yongsong Li, Zhengzhou Li, Jie Li, Junchao Yang, Abubakar Siddique
Summary: This paper proposes a weighted adaptive ring top-hat transformation (WARTH) for extracting infrared small targets in complex backgrounds. The WARTH method effectively measures local and global feature information using an adaptive ring-shaped structural element and a target awareness indicator, resulting in accurate detection of small targets with minimized false alarms.
Article
Engineering, Electrical & Electronic
Yu Wang, Zhen Qin, Jun Tao, Yili Xia
Summary: In this paper, an enhanced sparsity-aware recursive least squares (RLS) algorithm is proposed, which combines the proportionate updating (PU) and zero-attracting (ZA) mechanisms, and introduces a general convex regularization (CR) function and variable step-size (VSS) technique to improve performance.
Article
Engineering, Electrical & Electronic
Neil J. Bershad, Jose C. M. Bermudez
Summary: This paper analyzes the impact of processing delay on the Least Mean Squares (LMS) algorithm in system identification, highlighting bias issues in the resulting weight vector.
Article
Engineering, Electrical & Electronic
Kanghui Jiang, Defu Jiang, Mingxing Fu, Yan Han, Song Wang, Chao Zhang, Jingyu Shi
Summary: In this paper, a novel method for velocity estimation using multicarrier signals in a single dwell is proposed, which effectively addresses the issue of Doppler ambiguity in pulse Doppler radars.
Article
Engineering, Electrical & Electronic
Xiao-Jun Zhang, Peng-Lang Shui, Yu-Fan Xue
Summary: This paper proposes a method for low-velocity small target detection in maritime surveillance radars. It models sea clutter sequences using the spherical invariant random vector (SIRV) model with block tridiagonal speckle covariance matrix and inverse Gamma distributed texture. The proposed detector, which is a long-time adaptive generalized likelihood ratio test with linear threshold detector (GLRT-LTD), shows competitive detection performance in experiments.
Article
Engineering, Electrical & Electronic
Aiyi Zhang, Fulai Liu, Ruiyan Du
Summary: This paper proposes an adaptive weighted robust data recovery method with total variation regularization for hyperspectral image. The method models the HSI recovery problem as a tensor robust principal component analysis optimization problem, decomposing the data into low-rank HSI data, outliers, and noise component. An adaptive weighted strategy is then defined to impose on the tensor nuclear norm and outliers, using the priori information of singular values and strengthening the sparsity of outliers.
Article
Engineering, Electrical & Electronic
Hamid Asadi, Babak Seyfe
Summary: This paper presents a novel approach for estimating the model order in the presence of observation errors. The proposed method is based on correntropy estimation of eigenvalues in the observation space, which is further enhanced by resampling the observations using the bootstrap method. The algorithm partitions the observation space into signal and noise subspaces using the covariance matrix of mixtures, and determines the model order based on a correntropy estimator with kernel functions. Theoretical analysis and comparative evaluations demonstrate the superiority of this information-theoretic approach.
Article
Engineering, Electrical & Electronic
Buket colak Guvenc, Engin Cemal Menguc
Summary: In this paper, a novel family of online censoring based complex-valued least mean kurtosis (CLMK) algorithms is proposed. The algorithms censor less informative complex-valued data streams and reduce the costs of data processing without affecting accuracy. Robust algorithms are also developed to handle outliers. The simulation results confirm the attractive features of the proposed algorithms in large-scale system identification and regression scenarios.
Article
Engineering, Electrical & Electronic
Yun Su, Weixian Tan, Yifan Dong, Wei Xu, Pingping Huang, Jianxin Zhang, Diankun Zhang
Summary: In this study, a novel method for detecting low-resolution and small targets in millimeter wave radar images is proposed. The Wavelet-Conv structure and Wavelet-Attention mechanism are introduced to overcome the limitations of existing detectors. Experimental results demonstrate that the proposed method improves recall and mean average precision while maintaining competitive inference speed.
Article
Engineering, Electrical & Electronic
Xin Wang, Xingxing Jiang, Qiuyu Song, Jie Liu, Jianfeng Guo, Zhongkui Zhu
Summary: This study proposes a variational mode extraction (VME) method for extracting specific modes from complicated signals. By exploring the convergence property of VME, strategies for identifying ICF and determining the balance parameter are designed, and a bandwidth estimation strategy is constructed. The effectiveness of the proposed method for bearings fault diagnosis is verified and compared with other methods.