NDLI: Learning to Diversify Patch-Based Priors for Remote Sensing Image Restoration

Content Provider	IEEE Xplore Digital Library
Author	Ping Zhong Ni Peng Runsheng Wang
Copyright Year	2008
Abstract	The restoration of clean remote sensing images from the degraded images is usually an ill-posed problem. A feasible solution for the problem is incorporating various priors into restoration procedure as constrained conditions. Recently, the patch-based Gaussian mixture model (GMM) priors became popular. However, the learning of patch-based GMM priors usually assumes the components of the GMM are independent and identically distributed (i.i.d.). The assumption means that two components could model the same training samples, and thus a possible decrease in model's description ability. This work presents a new learning method to diversify the patch-based GMM priors through modeling the GMM's parameters as a determinantal point process (DPP). The introduction of DPP allows us to specify a preference for diversity in patch-based GMM using a positive definite kernel function and thus to increase the model's description ability for a given model complexity. Furthermore, under the expected patch log-likelihood (EPLL) restoration framework, we further introduce the diversified patch-based GMM priors into remote sensing literature and develop new remote sensing image restoration algorithms. The extensive experiments for denoising and deblurring of remote sensing images demonstrated that the proposed algorithms produced better restoration results than other methods that already proven to perform well in the literature.
Starting Page	5225
Ending Page	5245
Page Count	21
File Size	8100701
File Format	PDF
ISSN	19391404
Volume Number	8
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Image restoration Kernel Computational modeling Noise reduction Gaussian mixture model Algorithm design and analysis restoration Determinantal point process (DPP) diversity Gaussian mixture model (GMM) machine learning prior remote sensing image
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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