An Improved Variational Method for Image Denoising
For researchers in image processing, this is an incremental improvement over existing TV methods for denoising.
The paper proposes a Mixed-norm TV (MixTV) model for image denoising that effectively removes multiple noise types and their combinations, with numerical experiments showing improved denoising quality compared to other TV models.
The total variation (TV) method is an image denoising technique that aims to reduce noise by minimizing the total variation of the image, which measures the variation in pixel intensities. The TV method has been widely applied in image processing and computer vision for its ability to preserve edges and enhance image quality. In this paper, we propose a Mixed-norm TV (MixTV) model for image denoising and the associated numerical algorithm to carry out the procedure, which is particularly effective in removing several types of noise and their combinations. Our MixTV admits a unique solution and the associated numerical algorithm guarantees convergence. Numerical experiments are demonstrated to show improved effectiveness and denoising quality compared to other TV models. Such encouraging results further enhance the utility of the TV method in image processing. Our project page is available at https://angusbb.github.io/MixTV.