Image Restoration With Total Variation and Iterative Regularization Parameter Estimation

Regularization techniques are widely used for solving ill-posed image processing problems and in particular for image noise removal. Total variation (TV) regularization is one of the foremost edge preserving methods for noise removal from images that can overcome the over-smoothing effects of the classical Tikhonov regularization. One of the important aspects in this approach is the involvement of the regularization parameter that needs to be set appropriately to obtain optimal restoration results. In this work, we utilize a fast split Bregman based implementation of the TV regularization for denoising along with an iterative parameter estimation from local image information. Experimental results on a variety noisy images indicate the promise of our TV regularization with iterative parameter estimation with local variance method, and comparison with related schemes show better edge preservation and robust noise removal.

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