In this paper we study an algorithm for solving a minimization problem composed of a
differentiable (possibly nonconvex) and a convex (possibly nondifferentiable) function. The …

Galić et al.(Journal of Mathematical Imaging and Vision 31: 255–269, 2008) have shown
that compression based on edge-enhancing anisotropic diffusion (EED) can outperform the …

Recently, deep learning based inpainting methods have shown promising performance, in
which some multi-scale networks are introduced to characterize image content in both …

The deep biosphere of the subseafloor crust is believed to contain a significant part of
Earth's biomass, but because of the difficulties of directly observing the living organisms, its …

We investigate numerous structural connections between numerical algorithms for partial
differential equations (PDEs) and neural architectures. Our goal is to transfer the rich set of …

This paper proposes a color-direction patch-sparsity-based image inpainting method to
better maintain structure coherence, texture clarity, and neighborhood consistence of the …

Finding optimal data for inpainting is a key problem in the context of partial differential
equation-based image compression. We present a new model for optimising the data used …

Diffusion-based inpainting is a powerful tool for the reconstruction of images from sparse
data. Its quality strongly depends on the choice of known data. Optimising their spatial …

Inpainting based image compression approaches, especially linear and non-linear diffusion
models, are an active research topic for lossy image compression. The major challenge in …

Harmonic inpainting with optimised data is very popular for inpainting-based image
compression. We improve this approach in three important aspects. Firstly, we replace the …