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Found 5 papers, 0 papers with code
Revisiting Nonlocal Self-Similarity from Continuous Representation
Extensive multi-dimensional data processing experiments on-meshgrid (e. g., image inpainting and image denoising) and off-meshgrid (e. g., climate data prediction and point cloud recovery) validate the versatility, effectiveness, and efficiency of our CRNL as compared with state-of-the-art methods.
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization
In the t-SVD, there are two key building blocks: (i) the low-rank enhanced transform and (ii) the accompanying low-rank characterization of transformed frontal slices.
S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning
In this work, we propose a self-supervised method that combines the capacities of deep denoiser and the generalization abilities of hand-crafted regularization for seismic data random noise attenuation.
Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery
To break this barrier, we propose a low-rank tensor function representation (LRTFR), which can continuously represent data beyond meshgrid with infinite resolution.
HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging
Inverse problems in multi-dimensional imaging, e. g., completion, denoising, and compressive sensing, are challenging owing to the big volume of the data and the inherent ill-posedness.
