Total Variation L1 Fidelity Salt-and-Pepper Denoising with Adaptive Regularization Parameter

Abstract

Total variation (TV) is an effective tool to solve the image denoising and many other image processing problems. For the denoising problem, it is necessary to create automatic image processing methods based on parameters estimation of the corresponding models. For TV image denoising problem, almost methods focus on TV-L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> norm. The works for parameter estimation of denoising model based on TV-L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> norm is very little. The denoising model with TV-L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> norm is impressive to treat the salt-and-pepper noise. In this paper, we propose a parameter estimation method based on characteristics of the salt-and-pepper noise. This method is especially effective for the images without very high contrast and with high noise level. We will handle the comparison to other salt-and-pepper denoising methods, such as TV-L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> method and the BPDF method to prove the effectiveness of the proposed parameter estimation method for the adaptive TVL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> denoising model.