In this paper, an image recovery problem under the case of salt-and-pepper noise and data missing that degrade image quality is addressed if they are not effectively handled, where the salt-and-pepper noise as the impulsive noise is remodeled as a sparse signal due to its impulsiveness and the data missing pattern, denoted by a sparse vector, contains only zeros and ones to formulate the data missing. In particular, the salt-and-pepper noise and data missing are reformatted by their sparsity, respectively. The wavelet and framelet domains are explored to sparsely represent the image in order to accurately reconstruct the clean image. From the reformulations conducted and to recover the image, under one optimization framework, a joint estimation is developed to perform the image recovery, the salt-and-pepper noise suppression, and the missing patter estimation. To solve the optimization problem, two efficient solvers are developed to obtain the joint estimation solution, and they are based on the alternating direction method of multipliers (ADMM) and accelerated proximal gradient (APG). Finally, numerical studies verify that the joint estimation algorithm outperforms the state-of-the-art approaches in terms of both objective and subjective evaluation standards.
Research on Multiple States Joint Estimation Algorithm for Electric Vehicles Under Charge Mode
