|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | SAR Slow Moving Target Imaging Based on Over-Sampling Smooth Algorithm显示文摘A novel Synthetic aperture radar(SAR)signal processing technique has been proposed which refocused slow moving targets based on phase retrieval algorithm. After theoretical derivation, we can get that the raw data Fourier magnitude of slow moving targets is approximate to the stationary ones in the SAR system. By applying the Fourier magnitude of received data to phase retrieval algorithms, the blur and defocusing effect caused by the moving of the targets can be eliminated. The simulated results demonstrate the validity of this algorithm. | SHI Hongyin YANG Xiaoyan ZHOU Qiuxiao LIAN Qiusheng | 2017 | Chinese Journal of Electronics2017，26，4： | 3 |
| 2 | Single Image Rain Removal Using Image Decomposition and a Dense Network显示文摘Removing rain from a single image is a challenging task due to the absence of temporal information. Considering that a rainy image can be decomposed into the low-frequency(LF) and high-frequency(HF) components, where the coarse scale information is retained in the LF component and the rain streaks and texture correspond to the HF component, we propose a single image rain removal algorithm using image decomposition and a dense network. We design two task-driven sub-networks to estimate the LF and non-rain HF components of a rainy image. The high-frequency estimation sub-network employs a densely connected network structure, while the low-frequency sub-network uses a simple convolutional neural network(CNN).We add total variation(TV) regularization and LF-channel fidelity terms to the loss function to optimize the two subnetworks jointly. The method then obtains de-rained output by combining the estimated LF and non-rain HF components.Extensive experiments on synthetic and real-world rainy images demonstrate that our method removes rain streaks while preserving non-rain details, and achieves superior de-raining performance both perceptually and quantitatively. | Qiusheng Lian Wenfeng Yan Xiaohua Zhang Shuzhen Chen | 2019 | IEEE/CAA Journal of Automatica Sinica2019，6，6： | 1 |
| 3 | SAR imaging method for sea scene target based on improved phase retrieval algorithm显示文摘Due to the influence of the platform random motion and electromagnetic propagation in turbulent media, the synthetic aperture radar(SAR) high resolution imaging for the sea scenes where there are large amounts of water returns with some target(land) returns is very difficult. To solve this problem, a SAR imaging method based on the improved phase retrieval(PR) algorithm is proposed. First, a filter is added to the conventional PR algorithm which can reduce the influence of water returns on the reconstruction of the targets and improve the reconstruction result of the targets. Then, the corrupted phase of the Fourier transform of the intensity image in the iterative process, which can improve the stability of the iterative algorithm, is used to reduce the recovery errors, and a better recovery performance is achieved. Finally, several experiments are performed to demonstrate the advantages of the proposed method. | Hongyin Shi Qiuxiao Zhou Xiaoyan Yang Qiusheng Lian | 2016 | Journal of Systems Engineering and Electronics2016，27，6： | 0 |
| 4 | A New Method for Building-Level Population Estimation by Integrating LiDAR, Nighttime Light, and POI Data显示文摘Building-level population data are of vital importance in disaster management,homeland security,and public health.Remotely sensed data,especially LiDAR data,which allow measures of three-dimensional morphological information,have been shown to be useful for fine-scale population estimations.However,studies using LiDAR data for population estimation have noted a nonstationary relationship between LiDAR-derived morphological indicators and populations due to the unbalanced characteristic of population distribution.In this article,we proposed a framework to estimate population at the building level by integrating POI data,nighttime light(NTL)data,and LiDAR data.Building objects were first derived using LiDAR data and aerial photographs.Then,three categories of building-level features,including geometric features,nighttime light intensity features,and POI features,were,respectively,extracted from LiDAR data,Luojia1-01 NTL data,and POI data.Finally,a welltrained random forest model was built to estimate the population of each individual building.Huangpu District in Shanghai,China,was chosen to validate the proposed method.A comparison between the estimation result and reference data shows that the proposed method achieved a good accuracy with R^(2)=0:65 at the building level and R^(2)=0:79 at the community level.The NTL radiance intensity was found to have a positive relationship with population in residential areas,while a negative relationship was found in office and commercial areas.Our study has shown that by integrating both the three-dimensional morphological information derived from LiDAR data and the human activity information extracted from POI and NTL data,the accuracy of building-level population estimation can be improved. | Hongxing Chen Bin Wu Bailang Yu Zuoqi Chen Qiusheng Wu Ting Lian Congxiao Wang Qiaoxuan Li Jianping Wu | 2021 | Journal of Remote Sensing2021，，1： | 0 |
| 5 | Dual-Prior Integrated Image Reconstruction for Quanta Image Sensors Using Multi-Agent Consensus Equilibrium显示文摘Quanta image sensors(QIS) are a new type of singlephoton imaging device that can oversample the light field to generate binary bit-streams. The reconstruction for QIS refers to the recovery of original scenes from these binary measurements.Conventional reconstruction algorithms for QIS generally depend solely on one instantiated prior and are certainly insufficient for capturing the statistical properties over high-dimensional space.On the other hand, deep learning-based methods have shown promising performance, due to their excellent ability to learn feature representations from relevant databases. However, most deep models only focus on exploring local features while generally overlooking long-range similarity. In view of this, a dual-prior integrated reconstruction algorithm for QIS(DPI-QIS) is proposed, which combines a deep prior with a non-local self-similarity one using the multi-agent consensus equilibrium(MACE)framework. In comparison to the approaches that utilize a single prior, DPI-QIS fits the reconstruction model sufficiently by leveraging the respective merits of both priors. An effective yet flexible MACE framework is employed to integrate the physical forward model allying with the two prior-based models to achieve an overall better result. Extensive experiments demonstrate that the proposed algorithm achieves state-of-the-art performance in terms of objective and visual perception at multiple oversampling factors, while having stronger robustness to noise. | Dan Zhang Qiusheng Lian Yueming Su Tengfei Ren | 2023 | IEEE/CAA Journal of Automatica Sinica2023，10，6： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员