Denoising Very High Resolution Optical Remote Sensing Images: Application and Optimization of Nonlocal Bayes method

Very high resolution optical remote sensing images (RSI) are often corrupted by noise. Among popular denoising methods in the state of the art, nonlocal Bayes (NLB) has led to successful results on real datasets, with high quality and reasonable computation time. However, its computation time remain...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2018-03, Vol.11 (3), p.691-700
Main Authors: Masse, Antoine, Lefevre, Sebastien, Binet, Renaud, Artigues, Stephanie, Blanchet, Gwendoline, Baillarin, Simon
Format: Article
Language:English
Subjects:
Bayes methods
Bayesian analysis
Computation
Computational complexity
Computing time
Estimation
High resolution
Image denoising
Image resolution
Masking
Noise reduction
nonlocal Bayes (NLB)
Optical imaging
Optimization
Pléiades
Production scheduling
Remote sensing
Resolution
Submarine pipelines
very high resolution (VHR)
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Summary:Very high resolution optical remote sensing images (RSI) are often corrupted by noise. Among popular denoising methods in the state of the art, nonlocal Bayes (NLB) has led to successful results on real datasets, with high quality and reasonable computation time. However, its computation time remains prohibitive with respect to requirements of operational RSI pipelines, such as Pléiades one. In this paper, we tackle such an issue and introduce several optimizations aiming to significantly reduce the computation time required by NLB while keeping the best denoising quality (i.e., preserving edges, textures, and homogeneous areas). More precisely, our improvements consist of reducing multiple estimations of a same pixel with a masking technique and modifying the spatial extent of the similar patch search area (i.e., one of the main parts of nonlocal algorithms, such as NLB). We report several experiments and discuss optimal settings for these parameters, allowing a gain in computation time of 50% (resp. 15%) with optimized masking strategy (resp. spatial extent of the search area). When both contributions are combined, we achieve the same denoising quality as standard NLB while doubling the computation efficiency, the latter being increased fivefold if we accept a very small (lower than 0.1%) loss in quality.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2018.2793537