Noise Reduction using an Adaptive Iteration based on Bayesian Model

S.-H. Lee (Korea)


Noise Reduction, MRF, Point-Jacobian Iteration, SAR, Multiplicative Noise.


In this paper, an approach using a Bayesian model based on a Gaussian distribution for image intensity and a GRF for image texture is proposed for denoising the images corrupted by salty noise. The MRF is used to represent spatial correlation existing in many digital images and is incorporated into digital image analysis by viewing pixel type s as states of molecules in a lattice-like physical system defined on a GRF. Because of the MRF-GRF equivalence, the assignment of an energy function to the physical system determines its Gibbs measure, which is used to model molecular interactions. The proposed iterative approach was first evaluated using simulation data of speckle noise generated by the Monte Carlo method. The methodology was then applied to SAR data remotely sensed on Korean Peninsula. In the extensive experiments of this study, the proposed method demonstrated the capability to relax noise and estimate noise-free intensity.

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