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A Continuous Low-Rank Tensor Approach for Removing Clouds from Optical Remote Sensing Images
Remote SensingOptical remote sensing images are often partially obscured by clouds due to the inability of visible light to penetrate cloud cover, which significantly limits their subsequent applications. Most existing cloud removal methods formulate the problem using
Dong-Lin Sun +3 more
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Hyperspectral Image Denoising via
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThis article studies the mixed noise removal problem for hyperspectral images (HSIs), which often suffer from Gaussian noise and sparse noise. Conventional denoising models mainly employ the $L_{1}$-norm-based regularizers to remove sparse noise and ...
Xin Tian, Kun Xie, Hanling Zhang
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Superresolution of Radar Forward-Looking Imaging Based on Accelerated TV-Sparse Method
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Total variation-sparse (TV-sparse)-based multiconstraint devonvolution method has been used to realize superresolution imaging and preserve target contour information simultaneously of radar forward-looking imaging.
Yin Zhang +4 more
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, 2013 We show that it can be decided in polynomial time whether a graph of maximum degree 6 has a square root; if a square root exists, then our algorithm finds one with minimum number of edges. We also show that it is FPT to decide whether a connected n-vertex graph has a square root with at most n − 1 + k edges when this problem is parameterized by k ...
Cochefert, M. +4 more
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Direct Sparse Deblurring [PDF]
Journal of Mathematical Imaging and Vision, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lou, Yifei +2 more
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IEEE Access, 2020 Extreme learning machine (ELM) is a simple feedforward neural network, and it has been extensively used in applications for its extremely fast learning speed and good generalization performance.
Qinwei Fan, Lei Niu, Qian Kang
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Micromachines, 2023 Recently, the layer-wise N:M fine-grained sparse neural network algorithm (i.e., every M-weights contains N non-zero values) has attracted tremendous attention, as it can effectively reduce the computational complexity with negligible accuracy loss ...
Xiaoru Xie +3 more
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Remote SensingEfficiently removing clouds from remote sensing imagery presents a significant challenge, yet it is crucial for a variety of applications. This paper introduces a novel sparse function, named the tri-fiber-wise sparse function, meticulously engineered ...
Dong-Lin Sun, Teng-Yu Ji, Meng Ding
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Quantum Machine Intelligence, 2022 Abstract The ultimate goal of any sparse coding method is to accurately recover from a few noisy linear measurements, an unknown sparse vector. Unfortunately, this estimation problem is NP-hard in general, and it is therefore always approached with an approximation method, such as lasso or orthogonal matching pursuit, thus trading off accuracy ...
Romano, Yaniv +7 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018 ** Corrected a bug which caused the real-time results for ORB-SLAM (dashed lines in Fig. 10 and 12) to be much worse than they should be ** Added references [12], [13],[19], and Fig. 11. ** Partly re-formulated and extended [5. Conclusion].
Jakob Engel +2 more
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