Image Restoration Using Space-Variant Gaussian Scale Mixtures in over complete Pyramids

Abstract:-

In recent years, Bayes least squares-Gaussian scale mixtures (BLS-GSM) have 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 BLS estimator using space-variant GSM. The model can be also applied to image deconvolution, by first performing global blur compensation, and then doing local adaptive enoising. We demonstrate through simulations that the proposed method, besides being model-based and non iterative, it is also robust and efficient. Its performance measured visually and in L2-norm terms, is significantly higher than the original BLS-GSM method, both for denoising and deconvolution.





Existing System:-

• Wiener filtering is one of the simplest model-based restoration methods, and yet it provides optimal results when dealing with Gaussian signals and noise.

Proposed System:-

• Here we proposed an adaptive scalar Wiener method in the pixel domain, based on estimating the local variance at every spatial location of the image.

Software Specifications:-
Operating System : Windows XP
Front end : JAVA

Hardware Specifications:-
System Processor : Pentium IV
Processor Speed : 2.80GHz