Image Restoration Using Space-Variant Gaussian Scale Mixtures in Overcomplete Pyramids

In recent years Bayes Least Squares - Gaussian scale mixtures (BLS-GSM) has emerged as one of the most powerful methods for image restoration. Its strength relies on providing a simple and yet very effective local statistical description of oriented pyramid coefficient neighborhoods via a GSM vector. This can be viewed as a fine adaptation of the model to the signal variance at each scale, orientation and spatial location. Here we present an enhancement of the model by introducing a coarser adaptation level, where a larger neighborhood is used to estimate the local signal covariance within every subband. We formulate our model as a Bayes least squares estimator using space-variant Gaussian scale mixtures. The model can be also applied to image deconvolution, by first performing a global blur compensation, and then doing local adaptive denoising. We demonstrate through simulations that the proposed method, besides being model-based and non-iterative, it is also robust and efficient. Its...

• Type: Journal
• Year: 2008
• Where: TIP
• Authors: Jose A. Guerrero-Colon, Luis Mancera, Javier Portilla
• Keywords: Bayes Least Squares, Gaussian Scale, Model, TIP 2008