Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The authors present a comparison beten stochastic and deterministic relaxation algorithms for maximum a posteriori estimation of gray-level images modeled by noncausal Gauss-Markov random fields (GMRF) and corrupted by film grain noise. The degradation involves nonlinear transformation and multiplicative noise. Parameters for the GMRF model re estimated from the original image using maximum-likelihood techniques. To overcome modeling errors, a constraint minimization approach is suggested for estimating the parameters to ensure the positivity of the por spectral density function. Real image experiments with various noise variances and magnitudes of the nonlinear transformation are presented. >
['T. Simchony', 'Ramalingam Chellappa', "Ze'ev Lichtenstein"]
I am writing a research paper on a idea Relaxation algorithms for MAP estimation of gray-level images with multiplicative noise
[{"from": "human", "value": "I am writing a research paper on a idea Relaxation algorithms for MAP estimation of gray-level images with multiplicative noise"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The authors present a comparison beten stochastic and deterministic relaxation algorithms for maximum a posteriori estimation of gray-level images modeled by noncausal Gauss-Markov random fields (GMRF) and corrupted by film grain noise. The degradation involves nonlinear transformation and multiplicative noise. Parameters for the GMRF model re estimated from the original image using maximum-likelihood techniques. To overcome modeling errors, a constraint minimization approach is suggested for estimating the parameters to ensure the positivity of the por spectral density function. Real image experiments with various noise variances and magnitudes of the nonlinear transformation are presented. >"}]