Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage
By Shen-En Qian. IEEE Transactions on Geoscience and Remote Sensing, 2011
416 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/e1fec22da5aebc4d76466a49640b193ffc65bb0c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Jocelyn Chanussot
- Liangpei Zhang
- Huanfeng Shen
- Hongyan Zhang
- Qiangqiang Yuan
- Wei He
- Pedram Ghamisi
- Behnood Rasti
In Media Technology
- Jocelyn Chanussot
- Liangpei Zhang
- Pedram Ghamisi
- Huanfeng Shen
- Behnood Rasti
- Qiangqiang Yuan
- Hongyan Zhang
- Paul Scheunders
Citations in the corpus
- Minimum Noise Fraction versus Principal Component Analysis as a Preprocessing Step for Hyperspectral Imagery Denoising (2016, methodology): “As a result, Chen and Qian (2011), Chen et al. (2014), and Chen et al. (2011) successfully improved this method in their previous works by combining PCA with block matching and 3D Filtering (BM3D) and BM4D.”
- Hyperspectral Image Restoration Using Low-Rank Matrix Recovery (2014, background): “The differences in the spectral signatures between the noise-free and the restoration results are calculated [11].”
- Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds (2015, background): “There are many random noises produced from instruments or due to variable physical properties of the samples (Chen 2011).”
- Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model (2012, methodology): “Recently, Chen et al. [11] proposed a new hyperspectral image denoising algorithm by adding a PCA transform before using wavelet shrinkage; first, a PCA transform was implemented on the original hyperspectral image, and then, the low-energy PCA output channel was denoised with wavelet shrinkage…”
- Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration (2016, methodology): “In the method proposed in [32], PCA is first applied to the HSI, and the low-variance PCs are subsequently filtered by a 2-”
- Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition (2017, background): “Additionally, several advanced techniques in traditional image processing, including the nonlocal similarity [10], wavelet shrinkage [11], anisotropic diffusion [12], have been adopted for HSI restoration in recent years.”
- Noise Reduction in Hyperspectral Imagery: Overview and Application (2018, methodology): “In [32], 2D DWT and principal component analysis (PCA) were used to decorrelate the noise and the signal spatially and spectrally, receptively.”