Joint Total Variation and Hessian Norm Regularized Bayesian Approach for Mixed Gaussian-Impulse Noise Removal

Le résumé fourni par la source

Mixed noise frequently affects optical images, complicating their interpretation and analysis. Among the most prevalent types of mixed noise are Gaussian and impulse noise. which can significantly degrade image quality. This study proposes a novel Bayesian method for removing mixed Gaussian-Impulse noise from optical images. The approach utilizes total variation (TV) and the nuclear norm of the Hessian matrix as regularization parameters within an optimization framework. These parameters are derived from maximum a posteriori (MAP) estimations of the noise statistics. TV regularization ensures the smoothness of the solution while incorporating the Hessian matrix, which helps preserve fine details in the final optimized image. The problem is then addressed using primal-dual algorithms, which efficiently solve the proposed optimization problem. Experimental results demonstrate that the proposed method significantly improves image restoration quality compared to existing denoising techniques. The study's findings indicate that with its sophisticated regularization and optimization strategies. this Bayesian approach offers a robust solution for effectively denoising optical images contaminated with mixed Gaussian-Impulse noise.

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