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.
Image deblurring under impulse noise is a typical ill-posed problem which requires regularization methods to guarantee high-quality imaging. L1-norm data-fidelity term and total variation (TV) regularizer have been combined to contribute the popular regularization method. However, the TV-regularized variational image deblurring model often suffers from the staircase-like artifacts leading to image quality degradation. To enhance image quality, the detailpreserving total generalized variation (TGV) was introduced to replace TV to eliminate the undesirable artifacts. The resulting nonconvex optimization problem was effectively solved using the alternating direction method of multipliers (ADMM). In addition, an automatic method for selecting spatially adapted regularization parameters was proposed to further improve deblurring performance. Our proposed image deblurring framework is able to remove blurring and impulse noise effects while maintaining the image edge details. Comprehensive experiments have been conducted to demonstrate the superior performance of our proposed method over several state-of-the-art image deblurring methods.
Imaging quality is often significantly degraded under hazy weather condition. The purpose of this paper is to recover the latent sharp image from its hazy version. It is well known that the accurate estimation of depth information could assist in improving dehazing performance. In this paper, a detail-preserving variational model was proposed to simultaneously estimate haze-free image and depth map. In particular, the total variation (TV) and total generalized variation (TGV) regularizers were introduced to restrain haze-free image and depth map, respectively. The resulting nonsmooth optimization problem was efficiently solved using the alternating direction method of multipliers (ADMM). Comprehensive experiments have been conducted on realistic datasets to compare our proposed method with several state-of-the-art dehazing methods. Results have illustrated the superior performance of the proposed method in terms of visual quality evaluation.
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