In this paper, we present a noise removal technique by combining the P-M model with the LLT model. The combined technique takes full use of the advantage of both filters which is able to preserve edges and simultaneou...In this paper, we present a noise removal technique by combining the P-M model with the LLT model. The combined technique takes full use of the advantage of both filters which is able to preserve edges and simultaneously overcomes the staircase effect. We use a weighting function in our model, and compare this model with the P-M model as well as other fourth-order functional both in theory and numerical experiment.展开更多
We propose an efficient gradient-type algorithm to solve the fourth-order LLT denoising model for both gray-scale and vector-valued images.Based on the primal-dual formulation of the original nondifferentiable model,t...We propose an efficient gradient-type algorithm to solve the fourth-order LLT denoising model for both gray-scale and vector-valued images.Based on the primal-dual formulation of the original nondifferentiable model,the new algorithm updates the primal and dual variables alternately using the gradient descent/ascent flows.Numerical examples are provided to demonstrate the superiority of our algorithm.展开更多
文摘In this paper, we present a noise removal technique by combining the P-M model with the LLT model. The combined technique takes full use of the advantage of both filters which is able to preserve edges and simultaneously overcomes the staircase effect. We use a weighting function in our model, and compare this model with the P-M model as well as other fourth-order functional both in theory and numerical experiment.
文摘We propose an efficient gradient-type algorithm to solve the fourth-order LLT denoising model for both gray-scale and vector-valued images.Based on the primal-dual formulation of the original nondifferentiable model,the new algorithm updates the primal and dual variables alternately using the gradient descent/ascent flows.Numerical examples are provided to demonstrate the superiority of our algorithm.
