A Nonlocal Self-Similarity-Based Weighted Tensor Low-Rank Decomposition for Multichannel Image Completion With Mixture Noise.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3172184
Abstract: Multichannel image completion with mixture noise is a challenging problem in the fields of machine learning, computer vision, image processing, and data mining. Traditional image completion models are not appropriate to deal with this problem… read more here.
Keywords: noise; nonlocal self; self similarity; image completion ... See more keywords
Revisiting Nonlocal Self-Similarity from Continuous Representation
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2024.3464875
Abstract: Nonlocal self-similarity (NSS) is an important prior that has been successfully applied in multi-dimensional data processing tasks, e.g., image and video recovery. However, existing NSS-based methods are solely suitable for meshgrid data such as images… read more here.
Keywords: self similarity; nonlocal self; continuous representation; similarity ... See more keywords
Seismic Data Denoising Based on Sparse and Low-Rank Regularization
Sign Up to like & getrecommendations! Published in 2020 at "Energies"
DOI: 10.3390/en13020372
Abstract: Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications. For the past ten years, there have mainly been two… read more here.
Keywords: self similarity; seismic denoising; seismic data; low rank ... See more keywords