This paper develops a Bayesian retinex algorithm for enhancing single underwater image
with multiorder gradient priors of reflectance and illumination. First, a simple yet effective …

Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

Recent advances in technology have enabled the combination of positron emission
tomography (PET) with magnetic resonance imaging (MRI). These PET-MRI scanners …

Magnetic resonance imaging (MRI) is a versatile imaging technique that allows different
contrasts depending on the acquisition parameters. Many clinical imaging studies acquire …

We introduce a novel generic energy functional that we employ to solve inverse imaging
problems within a variational framework. The proposed regularization family, termed as …

Image denoising models based on the total variation (TV) regularization have been used in
many fields of image processing. The main advantage of the TV regularization can preserve …

To improve the image segmentation quality, it is important to adequately describe the local
features of targets in images. In this paper, we develop a novel adaptive total variation …

Denoising plays a vital role in freeform structured surface metrology. Traditional techniques,
such as Gaussian and partial differential equation-based diffusion filters, often involve a time …

Even after two decades, the total variation (TV) remains one of the most popular
regularizations for image processing problems and has sparked a tremendous amount of …

To keep local structures when denoising the degraded image, we propose a new
anisotropic total variation (TV)-based restored model based on the combination of the …