Hybrid Deep Learning for Image Denoising: Balancing Noise Removal and Detail Preservation with Game Theory
Image denoising aims to suppress noise while preserving fine details, a challenge for multimedia and computer vision applications.
image denoising
Total fractional-order variation and bilateral filter for image denoising
Image denoising stands out as a primary goal in image processing. However, many existing methods encounter challenges in preserving features such as corners and edges of an image while deleting the noise. This study investigates and evaluates a fractional-order derivative based on the total $\alpha$-order variation (TV) model and the bilateral total variation (BTV) model. This choice is motivated by the proven effectiveness of the TV model in noise removal and edge preservation, with the BTV model further utilized to enhance the restoration of fine and intricate deta
Time-fractional diffusion equation for signal and image smoothing
In this paper, we utilize a time-fractional diffusion equation for image denoising and signal smoothing. A discretization of our model is provided. Numerical results show some remarkable results with a great performance, visually and quantitatively, compared to some well known competitive models.