In silico prediction of self-interacting proteins (SIPs) has become an important part of
proteomics. There is an urgent need to develop effective and reliable prediction methods to …

Multifractal analysis has traditionally been based on the pointwise Hölder regularity for
locally bounded functions. However, recently an extension to the rectangular pointwise …

In a recent past, Deep Learning emerged as a standard tool in Image Processing, commonly
involved in numerous and various tasks. Notably, Deep Learning has become increasingly …

While scale invariance is commonly observed in each component of real world multivariate
signals, it is also often the case that the inter-component correlation structure is not fractally …

We address the removal of canvas artifacts from high-resolution digital photographs and X-
ray images of paintings on canvas. Both imaging modalities are common investigative tools …

The last 50 years have seen an impressive development of mathematical methods for the
analysis and processing of digital images, mostly in the context of photography, biomedical …

This work examines the scaling properties of Mars topography through a wavelet-based
formalism. We conduct exhaustive one-dimensional (both longitudinal and latitudinal) and …

Multifractal analysis has become a powerful signal processing tool that characterizes signals
or images via the fluctuations of their pointwise regularity, quantified theoretically by the so …

The characterization and estimation of the Hölder regularity of random fields has long been
an important topic of Probability theory and Statistics. This notion of regularity has also been …

Scale-free dynamics, formalized by selfsimilarity, provides a versatile paradigm massively
and ubiquitously used to model temporal dynamics in real-world data. However, its practical …