Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization

@article{Xie2016MultispectralID,
  title={Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization},
  author={Qi Xie and Qian Zhao and Deyu Meng and Zongben Xu and Shuhang Gu and Wangmeng Zuo and Lei Zhang},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={1692-1700}
}
Multispectral images (MSI) can help deliver more faithful representation for real scenes than the traditional image system, and enhance the performance of many computer vision tasks. In real cases, however, an MSI is always corrupted by various noises. In this paper, we propose a new tensor-based denoising approach by fully considering two intrinsic characteristics underlying an MSI, i.e., the global correlation along spectrum (GCS) and nonlocal self-similarity across space (NSS). In specific… CONTINUE READING
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