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Juan Chen, Zhencai Zhu, Haiying Hu, Lin Qiu, Zhenzhen Zheng and Lei Dong
Infrared (IR) Image preprocessing is aimed at image denoising and enhancement to help with small target detection. According to the sparse representation theory, the IR original image is low rank, and the coefficient shows a sparse character. The low ran...
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Ziyi Li, Yang Li, Yanping Wang, Guangda Xie, Hongquan Qu and Zhuoyang Lyu
With the rapid development of deep learning, more and more complex models are applied to 3D point cloud object detection to improve accuracy. In general, the more complex the model, the better the performance and the greater the computational resource co...
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Yanfen Kong, Caiyue Zhou, Chuanyong Zhang, Lin Sun and Chongbo Zhou
The group sparse representation (GSR) model combines local sparsity and nonlocal similarity in image processing, and achieves excellent results. However, the traditional GSR model and all subsequent improved GSR models convert the RGB space of the image ...
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Raúl Ulices Silva-Ávalos, Hugo Enrique Júnez-Ferreira, Julián González-Trinidad and Carlos Bautista-Capetillo
In Mexico, agriculture in semi-arid regions is highly dependent on groundwater resources, where most of the aquifers? characterization is a pending task. In particular, the depth to the basement is unknown for most of the Mexican territory. Hence, the de...
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Qianqian Tong, Guannan Liang, Jiahao Ding, Tan Zhu, Miao Pan and Jinbo Bi
Regularized sparse learning with the l0
l
0
-norm is important in many areas, including statistical learning and signal processing. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex-constrained sparse learning due to their ...
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Yang Chen, Masao Yamagishi and Isao Yamada
This paper proposes a new group-sparsity-inducing regularizer to approximate l2,0
l
2
,
0
pseudo-norm. The regularizer is nonconvex, which can be seen as a linearly involved generalized Moreau enhancement of l2,1
l
2
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1
-norm. Moreover, the overall con...
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