This paper comprehensively reviews the recent development of image deblurring, including
non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques …

We propose IntraTomo, a powerful framework that combines the benefits of learning-based
and model-based approaches for solving highly ill-posed inverse problems in the Computed …

In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive,
hierarchical neural rendering pipeline for tomography. Through a combination of neural …

We introduce Neural Flow Maps, a novel simulation method bridging the emerging
paradigm of implicit neural representations with fluid simulation based on the theory of flow …

Recovering the 3D shape of transparent objects using a small number of unconstrained
natural images is an ill-posed problem. Complex light paths induced by refraction and …

Numerous techniques have been proposed for reconstructing 3D models for opaque objects
in past decades. However, none of them can be directly applied to transparent objects. This …

This article presents a versatile and robust SPH simulation approach for multiple-fluid flows.
The spatial distribution of different phases or components is modeled using the volume …

Despite significant recent progress, dense, time-resolved imaging of complex, non-
stationary 3D flow velocities remains an elusive goal. In this work we tackle this problem by …

X-ray computed tomography (CT) is a valuable tool for analyzing objects with interesting
internal structure or complex geometries that are not accessible with optical means …

Scattering effects in images, including those related to haze, fog and appearance of clouds,
are fundamentally dictated by microphysical characteristics of the scatterers. This work …