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DCGSD: Low-Light Image Enhancement with Dual-Conditional Guidance Sparse Diffusion Model

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Jin, Haiyan Wang, Jing Zuo, Fengyuan Su, Haonan Xiao, Zhaolin Wang, Bin Zhang, Yuanlin Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Department of Computer Science and Engineering NO. 5 South Jinhua Road Shaanxi Xi'an710048 China

When restoring low-light images, most methods largely overlook the ambiguity due to dark noise and lack discrimination for region and shape representations, resulting in invalid feature enhancement. In this work, we propose a physically explainable and prior guidance model for low-light image enhancement, termed Dual-Conditional Guidance Sparse Diffusion (DCGSD). Specifically, we introduce an elaborately designed Luminance Structure Guidance Head, which can be easily plugged into the existing diffusion model to emphasize the value of the luminance and structural representation. Furthermore, for reliable noise analysis, we provide a novel Sparse Attention Enhancement Module that is adaptively empowered to exploit the most useful region-to-region dependencies. This dynamic selection makes the diffusion process from dense to sparse, thus improving the efficiency of the reasoning noise distributions. To avoid noise amplification, we further present a Skip Calibration Module, which can be used to refine the local neighborhood that contains noisy and structural information. Extensive experiments have been performed to verify the superiority of the proposed method. DCGSD shows that leveraging dual-conditional guidance can support the diffusion model to produce sharper and more realistic results. © 1991-2012 IEEE.

关键词： Feature extraction

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A Quantum Temporal Difference Learning Method Based on Quantum World Model

A Quantum Temporal Difference Learning Method Based on Quant...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zeng, Peigen He, Ying Yu, F. Richard Du, Jianbo Shenzhen University College of Computer Science and Software Engineering Shenzhen China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Carleton University School of Information Technology Ottawa Canada

ISBN: (纸本)9798350351255

Based on quantum parallelism theory and quantum phenomena such as superposition and entanglement, quantum reinforcement learning (QRL) has the potential to surpass classical reinforcement learning (RL). Although some excellent works have been done on QRL, existing RL methods encounter chanllenges when performing in environments with sparse rewards. In this paper, we provide a new perspective on conducting temporal difference (TD) learning in quantum computing, which can eliminate redundant exploration steps compared to classical methods. Specifically, we first use environment information to construct a world model with quantum circuits, enabling it to interact in a quantum way. Then, we perform the learning process by using the quantum world model and Grover's algorithm to query backwards how to reach the recorded states with high TD-errors. Simulation results show that our proposed method has superior performance compared to classical RL algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Adaptive Region Sampling network For Polarimetric SAR Image Classification

Adaptive Region Sampling Network For Polarimetric SAR Image ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Junfei Shi Shanshan Ji Haiyan Jin Haonan Su Zhiyong Lv Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Deep learning models have demonstrated excellent performance for polarimetric SAR image classification. However, existing approaches generally use a fixed square window to sample image blocks as the network input, which may not effectively extract various terrain objects. To alleviate this issue, we proposed an adaptive region sampling network to learn different terrain types by introducing a novel sampling scheme with varying direction and scale. Initially, a complex PolSAR image is segmented into homogeneous, heterogeneous and boundary regions. Subsequently, small-scale and large-scale sampling windows are designed for homogeneous and heterogeneous regions, to capture local and global features for two types of regions respectively. Additionally, an adaptive directional sampling window is designed for boundary regions to ensure context consistency in the image block and prevent edge confusion. Experiments conducted on real PolSAR data sets demonstrate that our method achieves superior classification results, providing both regional consistency and boundary preservation.

关键词： Representation learning Deep learning Image segmentation Adaptive systems Accuracy Image edge detection Semantics

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Region Partition based Hybrid Deep network for Polarimetric SAR Image Classification

Region Partition based Hybrid Deep Network for Polarimetric ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Junfei Shi Linjing Xu Haiyan Jin Wei Wang Rong Fei Shanshan Ji Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The Convolutional Neural network (CNN) model excels at learning local features, but struggles with capturing global large-scale features, particularly in extremely heterogeneous areas. In contrast, the Graph Convolution network (GCN) is an effective tool for PolSAR image classification, demonstrating a capability to learn large-scale global features *** learn effective features for both heterogenous terrain objects and edge details well, a novel region partition based hybrid deep network is proposed for adaptive learning features for boundary and non-boundary regions, which can learn both large-scale global features for extremely heterogeneous terrain objects and pixel-wise features for edge details. The proposed method can effectively partition a PolSAR image into boundary and non-boundary regions, and design a CNN and GCN subnetworks for them respectively. Subsequently, a unified network is designed to effectively fuse both the advantages of GCN and CNN to enhance classification performance. The experiments verify the proposed algorithm can achieve better performance than compared methods in both region homogeneity and boundary preservation.

关键词： Accuracy Image edge detection Semantics Neural networks Geoscience and remote sensing Radar polarimetry Classification algorithms

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Symmetric Positive Definite Convolution network for Polarimetric SAR Image Classification

Symmetric Positive Definite Convolution Network for Polarime...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Junfei Shi keyan Shen Haiyan Jin Wei Wang Zhenghao Shi Haonan Su Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Deep learning models have been widely applied to Polarimetric Synthetic Aperture Radar (PolSAR) image classification due to their excellent performance. However, unlike natural images, PolSAR data is a 3×3 covariance matrix for each resolution unit. Existing deep learning methods generally convert the covariance matrix into a vector as the input of neural networks, which destroys the correlation between channels and distorts the matrix structure. To alleviate this issue, we explore a Symmetric Positive Definite (SPD) convolution network for PolSAR images, which directly inputs the PolSAR complex matrix into the network to learn the geometric features in Riemannian space. Furthermore, a CNN-enhanced SPDnet is designed to further learn the contextual high-level features, which can convert Riemannian matrix features into Euclidean space and apply them for classification. Experimental results on real PolSAR data sets demonstrate the proposed method can achieve better performance than the state-of-the-art methods.

关键词： Deep learning Symmetric matrices Convolution Semantics Neural networks Vectors Radar polarimetry

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CNN-Enhanced Deep Sparse Representation network for Polarimetric SAR Image Classification

CNN-Enhanced Deep Sparse Representation Network for Polarime...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Junfei Shi Mengmeng Nie Haiyan Jin Junhuai Li Yuanlin Zhang Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Deep learning networks can automatically acquire high-level semantic features for polarimetric SAR image classification, while it involves a blind learning procedure without explicit guidance. In contrast, sparse representation methods represent effective non-deep models with a robust mathematical mechanism serving as guidance. However, they can’t capture complex image features and semantic information. To address these issues, we propose a novel approach known as the CNN-enhanced Deep Sparse Representation network (CE-DSRNet) for PolSAR image classification, which a Sparse Representation (SR) guided deep learning model. Initially, a sparse representation model is constructed for PolSAR images to capture essential features. Subsequently, to solve the sparse model, a Deep Sparse Representation network (DSRNet) is devised by transforming the Soft Threshold Iterative (ISTA) optimization procedure into a network, enabling automatic learning of sparse coefficients as features. Finally, a CNN-enhanced DSRNet is introduced, integrating DSRNet with CNN to effectively extract deep semantic features and enhance classification accuracy. Experiments demonstrate the effectiveness of the proposed method compared to state-of-the-art approaches.

关键词： Deep learning Sparse approximation Semantics Feature extraction Mathematical models Radar polarimetry Iterative algorithms

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A Caching Strategy Based on Many-to-Many Matching Game in D2D networks

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Tsinghua Science and technology 2021年第6期26卷 857-868页

作者： Kaihang Yu Zhonggui Ma Runyu Ni Tao Zhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Key Lab of Information Network Security Ministry of Public SecurityShanghai 201204China

Wireless edge caching has been proposed to reduce data traffic congestion in backhaul links, and it is being envisioned as one of the key components of next-generation wireless networks. This paper focuses on the influences of different caching strategies in Device-to-Device(D2D) networks. We model the D2D User Equipments(DUEs) as the Gauss determinantal point process considering the repulsion between DUEs, as well as the caching replacement process as a many-to-many matching game. By analyzing existing caching placement strategies, a new caching strategy is proposed, which represents the preference list of DUEs as the ratio of content popularity to cached probability. There are two distinct features in the proposed caching strategy.(1) It can cache other contents besides high popularity contents.(2) It can improve the cache hit ratio and reduce the latency compared with three caching placement strategies: Least Recently Used(LRU), Equal Probability Random Cache(EPRC), and the Most Popular Content Cache(MPC). Meanwhile, we analyze the effect of caching on the system performance in terms of different content popularity factors and cache capacity. Simulation results show that our proposed caching strategy is superior to the three other comparison strategies and can significantly improve the cache hit ratio and reduce the latency.

关键词： caching strategy Device-to-Device(D2D)networks Gauss determinantal point process many-to-many matching game

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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Laryngeal Leukoplakia Classification Via Dense Multiscale Feature Extraction in White Light Endoscopy Images 48

Laryngeal Leukoplakia Classification Via Dense Multiscale Fe...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： You, Zhenzhen Yan, Yan Shi, Zhenghao Zhao, Minghua Yan, Jing Liu, Haiqin Hei, Xinhong Ren, Xiaoyong Xi'An University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an China Xi'An Jiaotong University Second Affiliated Hospital of Medical College Department of Otorhinolaryngology Xi'an China

ISBN: (纸本)9781728163277

Laryngeal leukoplakia classification is challenging using white light endoscopy images. Relevant research focus on normal tissues versus non normal tissues, cancer versus non cancer classification. The objective of this paper is to classify laryngeal leukoplakia in white light endoscopy images into six classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia and squamous cell carcinoma. We proposed a dense multiscale convolutional neural network including parallel multiscale convolution, dense convolution and recurrent convolution in favor of extracting dense multiscale features of laryngeal leukoplakia for fine classification. The proposed network achieved an overall accuracy of 0.8958 for the six-class classification. It has high sensitivity and specificity for each class which are, respectively, 1.0000 and 0.9394 for normal tissues, 0.6667 and 1.0000 for inflammatory keratosis, 0.8889 and 0.9744 for mild dysplasia and moderate dysplasia, 0.7500 and 1.0000 for severe dysplasia, 1.0000 and 0.9767 for squamous cell carcinoma. The experimental results show that our proposed model is superior to the state-of-the-art deep learning-based models. © 2023 IEEE.

关键词： Endoscopy

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Cross Elitist Learning Multifactorial Evolutionary Algorithm 1

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3rd International Conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Li, Wei Luo, Haonan Wang, Lei School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China

ISBN: (数字)9789811961427

ISBN: (纸本)9789811961410

Multifactorial Evolutionary Algorithm (MFEA) is a popular optimization algorithm in recent years. It has implicit parallelism and can solve different problems at the same time in the same search space. However, premature convergence is a significant shortcoming of MFEA. The main reason for this phenomenon is the lack of population diversity and the inability to migrate effectively between tasks. To address this shortcoming, this paper proposes a cross elitist learning multifactorial evolutionary algorithm (CEL-MFEA). Firstly, the cross elitist strategy guides the individuals to learn from the elite individual, which can improve the convergence of the proposed CEL-MFEA. Secondly, the Nelder-Mead algorithm is used to provide an effective knowledge transfer between different tasks. Finally, the opposition learning mechanism is employed to ensure population diversity. The comprehensive ability of the proposed CEL-MFEA is evaluated by 9 classical multifactorial optimization problem test sets. Experimental results show that the CEL-MFEA is more competitive than MFEA algorithm and several popular multifactorial optimization algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Evolutionary algorithms

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