Improved decision-based detail-preserving variational method for removal of random-valued impulse noise

The authors propose an improved decision-based detail-preserving variational method (DPVM) for removal of random-valued impulse noise. In the denoising scheme, adaptive centre weighted median filter (ACWMF) is first ameliorated by employing the variable window technique to improve its detection ability in highly corrupted images. Based on the improved ACWMF, a fast iteration strategy is used to classify the noise candidates and label them with different noise marks. Then, all the noise candidates are restored one-time by weight-adjustable detail-preserving variational method. The weights between the data-fidelity term and the smooth regularisation term of the convex cost-function in DPVM are decided by the noise marks. After minimisation, the restored image is obtained. Extensive simulation results show that the proposed method outperforms some existing algorithms, both in vision and quantitative measurements. Moreover, our method is faster than some decision-based DPVM. Therefore it can be ported into practical application easily.