Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation
- 18 March 2015
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Vol. 8 (6), 3050-3061
- https://doi.org/10.1109/jstars.2015.2398433
Abstract
Due to the low-dimensional property of clean hyperspectral images (HSIs), many low-rank-based methods have been proposed to denoise HSIs. However, in an HSI, the noise intensity in different bands is often different, and most of the existing methods do not take this fact into consideration. In this paper, a noise-adjusted iterative low-rank matrix approximation (NAILRMA) method is proposed for HSI denoising. Based on the low-rank property of HSIs, the patchwise low-rank matrix approximation (LRMA) is established. To further separate the noise from the signal subspaces, an iterative regularization framework is proposed. Considering that the noise intensity in different bands is different, an adaptive iteration factor selection based on the noise variance of each HSI band is adopted. This noise-adjusted iteration strategy can effectively preserve the high-SNR bands and denoise the low-SNR bands. The randomized singular value decomposition (RSVD) method is then utilized to solve the NAILRMA optimization problem. A number of experiments were conducted in both simulated and real data conditions to illustrate the performance of the proposed NAILRMA method for HSI denoising.Keywords
Funding Information
- National Basic Research Program of China (2011CB707105)
- 863 Program (2013AA12A301)
- National Natural Science Foundation of China (61201342, 41431175)
- Program for Changjiang Scholars and Innovative Research Team in University (IRT1278)
This publication has 49 references indexed in Scilit:
- Robust principal component analysis?Journal of the ACM, 2011
- Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix DecompositionsSiam Review, 2011
- A Singular Value Thresholding Algorithm for Matrix CompletionSIAM Journal on Optimization, 2010
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse ProblemsSIAM Journal on Imaging Sciences, 2009
- Principal Component AnalysisPublished by Wiley ,2005
- An Iterative Regularization Method for Total Variation-Based Image RestorationMultiscale Modeling & Simulation, 2005
- Image Quality Assessment: From Error Visibility to Structural SimilarityIEEE Transactions on Image Processing, 2004
- Anomaly detection from hyperspectral imageryIEEE Signal Processing Magazine, 2002
- Interference and noise-adjusted principal components analysisIEEE Transactions on Geoscience and Remote Sensing, 1999
- A transformation for ordering multispectral data in terms of image quality with implications for noise removalIEEE Transactions on Geoscience and Remote Sensing, 1988