Spatially adaptive total generalized variation-regularized image deblurring with impulse noise

Abstract

Image deblurring with impulse noise is a typical ill-conditioned problem that requires regularization techniques to guarantee stable and high-quality imaging. According to the statistical properties of impulse noise, an L1-norm data fidelity term and a total variation (TV) regularizer have been combined to contribute a popular regularization model. However, traditional TV-regularized variational models usually suffer from staircase-like artifacts in homogenous regions resulting in visual quality degradation. To eliminate undesirable artifacts, we propose a high-order variational model by replacing the TV with a detail-preserving total generalized variation (TGV) regularizer. To further enhance imaging performance, the spatially adaptive regularization parameters are automatically selected, based on local image features to promote the high-order TGV-regularized variational model. The resulting nonsmooth optimization problem is effectively handled using the alternating direction method of multipliers-based numerical method. The proposed variational model has the capacity to remove blurring and impulse noise effects while maintaining fine image details. Comprehensive experiments were conducted on both gray and color images to compare our proposed method with several state-of-the-art image restoration methods. Experimental results have demonstrated its superior performance in terms of quantitative and qualitative image quality evaluations.