Ziqi Li - IEEE Xplore Author Profile
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Ziqi Li

Publications
9
Citations
43
Publications by Year
20232025
Co-Authors:
Show All Co-Authors (20)
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Ziqi Li

Affiliation

School of Information Engineering
Chang'an University
Xi'an, China

Publication Topics

Biography

Ziqi Li received the B.Sc. degree from the School of Electronics and Information, Xi'an Technological University, Xi'an, China, in 2019. He is working toward the Ph.D. degree with the School of Information Engineering, Chang'an University, Xi'an. His research interests include image processing and object detection.(Based on document published on 17 June 2024).
Publications
9
Citations
43
Publications by Year
20232025
Co-Authors:
Show All Co-Authors (20)
Author's Published Works

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All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model

Yuanbo Wen;Tao Gao;Ziqi Li;Jing Zhang;Kaihao Zhang;Ting Chen

IEEE Transactions on Multimedia
Year: 2025 | Early Access Article |
Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration. Specifically, our model employs the contrastive ...Show More

All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model

Yuanbo Wen;Tao Gao;Ziqi Li;Jing Zhang;Kaihao Zhang;Ting Chen

Year: 2025 | Early Access Article |

A Novel Scene-aware Pedestrian Detection in Dense Scenes

Ting Chen;Jinghua Chen;Tao Gao;Shukang Zhu;Zongyang Guo;Zixiang Liu;Ziqi Li;Quanzhao Zhao

2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Year: 2024 | Conference Paper |
In crowded scenes, detecting pedestrians with high density and various occlusions is always an important yet challenging task. To further improve the performance of one-stage detectors in detecting crowded pedestrians, we propose a one-stage dense pedestrian detection network called YOLO- DensePed (You Only Look Once-Dense Pedestrian Detection) to further overcome shortcoming including limited rec...Show More

A Novel Scene-aware Pedestrian Detection in Dense Scenes

Ting Chen;Jinghua Chen;Tao Gao;Shukang Zhu;Zongyang Guo;Zixiang Liu;Ziqi Li;Quanzhao Zhao

Year: 2024 | Conference Paper |

A 9.45-ENOB 3.84-MS/s Ping-Pong Interleaving SAR ADC with Integrated Buffers and SPI for 96-Channel Neural Signal Acquisition

Ziqi Li;Xinyue Gu;Hongming Lyu

2024 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2024 | Conference Paper |
This paper presents a 10-bit 3.84-MS/s ping-pong interleaving successive approximation register (SAR) ADC designed for a 96-channel neural signal acquisition system. The ADC incorporates an input buffer and peripheral circuits which allow it to directly connect the front-end array. A ping-pong interleaving structure is adopted to double the sampling rate at a given SPI clock frequency while minimi...Show More

A 9.45-ENOB 3.84-MS/s Ping-Pong Interleaving SAR ADC with Integrated Buffers and SPI for 96-Channel Neural Signal Acquisition

Ziqi Li;Xinyue Gu;Hongming Lyu

Year: 2024 | Conference Paper |

A Decision-Making Approach for Complex Unsignalized Intersection by Deep Reinforcement Learning

Shanke Li;Kun Peng;Fei Hui;Ziqi Li;Cheng Wei;Wenbo Wang

IEEE Transactions on Vehicular Technology
Year: 2024 | Volume: 73, Issue: 11 | Journal Article |
Cited by: Papers (2)
Decision-making for automatic vehicles at unsignalized intersections with dense traffic is one of the most challenging tasks. Due to the complex structure and frequent traffic accidents, traditional rule-based methods struggle to address this issue flexibly and often produce suboptimal strategies. Recently, deep reinforcement learning (DRL) has garnered significant attention for its exceptional pe...Show More

A Decision-Making Approach for Complex Unsignalized Intersection by Deep Reinforcement Learning

Shanke Li;Kun Peng;Fei Hui;Ziqi Li;Cheng Wei;Wenbo Wang

Year: 2024 | Volume: 73, Issue: 11 | Journal Article |

Encoder-Minimal and Decoder-Minimal Framework for Remote Sensing Image Dehazing

Yuanbo Wen;Tao Gao;Ziqi Li;Jing Zhang;Ting Chen

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
Haze obscures remote sensing images, hindering valuable information extraction. To this end, we propose RSHazeNet, an encoder-minimal and decoder-minimal framework for efficient remote sensing image dehazing. Specifically, regarding the process of merging features within the same level, we develop an innovative module called intra-level transposed fusion module (ITFM). This module employs adaptive...Show More

Encoder-Minimal and Decoder-Minimal Framework for Remote Sensing Image Dehazing

Yuanbo Wen;Tao Gao;Ziqi Li;Jing Zhang;Ting Chen

Year: 2024 | Conference Paper |

Multi-Dimension Queried and Interacting Network for Stereo Image Deraining

Yuanbo Wen;Tao Gao;Ziqi Li;Jing Zhang;Ting Chen

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Eliminating the rain degradation in stereo images poses a formidable challenge, which necessitates the efficient exploitation of mutual information present between the dual views. To this end, we devise MQINet, which employs multi-dimension queries and interactions for stereo image deraining. More specifically, our approach incorporates a context-aware dimension-wise queried block (CDQB). This mod...Show More

Multi-Dimension Queried and Interacting Network for Stereo Image Deraining

Yuanbo Wen;Tao Gao;Ziqi Li;Jing Zhang;Ting Chen

Year: 2024 | Conference Paper |

A Self-Supplementary and Revised Network for Remote Sensing Object Detection

Tao Gao;Zixiang Liu;Guiping Wu;Ziqi Li;Yuanbo Wen;Lidong Liu;Ting Chen;Jing Zhang

IEEE Geoscience and Remote Sensing Letters
Year: 2024 | Volume: 21 | Journal Article |
Object detection is an essential and crucial task in interpretation of optical remote sensing images (RSIs). However, its performance is usually limited due to the complex background and multiscale characteristics of targets. To overcome these limitations, a self-supplementary and revised anchor-free detector is proposed. First, to reduce the computational cost of detection, a partial bottleneck (...Show More

A Self-Supplementary and Revised Network for Remote Sensing Object Detection

Tao Gao;Zixiang Liu;Guiping Wu;Ziqi Li;Yuanbo Wen;Lidong Liu;Ting Chen;Jing Zhang

Year: 2024 | Volume: 21 | Journal Article |

Attention-Free Global Multiscale Fusion Network for Remote Sensing Object Detection

Tao Gao;Ziqi Li;Yuanbo Wen;Ting Chen;Qianqian Niu;Zixiang Liu

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Cited by: Papers (13)
Remote sensing object detection (RSOD) encounters challenges in complex backgrounds and small object detection, which are interconnected and unable to address separately. To this end, we propose an attention-free global multiscale fusion network (AGMF-Net). Initially, we present a spatial bias module (SBM) to obtain long-range dependencies as a part of our proposal global information extraction mo...Show More

Attention-Free Global Multiscale Fusion Network for Remote Sensing Object Detection

Tao Gao;Ziqi Li;Yuanbo Wen;Ting Chen;Qianqian Niu;Zixiang Liu

Year: 2024 | Volume: 62 | Journal Article |

Encoder-Free Multiaxis Physics-Aware Fusion Network for Remote Sensing Image Dehazing

Yuanbo Wen;Tao Gao;Jing Zhang;Ziqi Li;Ting Chen

IEEE Transactions on Geoscience and Remote Sensing
Year: 2023 | Volume: 61 | Journal Article |
Cited by: Papers (24)
Current methods for remote sensing image dehazing confront noteworthy computational intricacies and yield suboptimal dehazed outputs, thereby circumscribing their pragmatic applicability. To this end, we propose encoder-free multiaxis physics-aware fusion network (EMPF-Net), a novel EMPF-Net that exhibits both light-weighted characteristics and computational efficiency. In our pipeline, we contend...Show More

Encoder-Free Multiaxis Physics-Aware Fusion Network for Remote Sensing Image Dehazing

Yuanbo Wen;Tao Gao;Jing Zhang;Ziqi Li;Ting Chen

Year: 2023 | Volume: 61 | Journal Article |

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