Deep learning has gained significant interest in image denoising, but there are notable
distinctions in the types of deep learning methods used. Discriminative learning is suitable …

Images are frequently corrupted by various sorts of mixed or unrecognized noise, including
mixed Poisson-Gaussian noise, rather than just a single kind of noise. In this work, we …

Image denoising is to recover true image from noisy image. Many image deonising models
are proposed during the last decades. Some models preserve the margin of tissue, ie, TV …

In this work, we propose a physics-informed solution for a blending PDE model with
application in image denoising and edge-preserving. The blending model contains isotropic …

The total variation model performs very well for removing noise while preserving edges.
However, it gives a piecewise constant solution which often leads to the staircase effect …

To address the staircasing problem in deblurred images generated by a simple total
variation (TV) based model, one approach is to use the total fractional-order variation …

Designing a technique with higher speckle noise suppressing capability, better edge
preserving performance, and lower time complexity is a research objective for the common …

░ ABSTRACT-Edge indicating operators such as gradient, mean curvature, and Gauss
curvature-based image noise removal algorithms are incapable of classifying edges, ramps …

Variational regularization, renowned for its sound theoretical foundations and impressive
performance, is widely used in image restoration. The traditional regularization models …

The image denoising model based on anisotropic diffusion equation often appears the
staircase effect while image denoising, and the traditional super-resolution reconstruction …