The following three factors restrict the application of existing low-light image enhancement
methods: unpredictable brightness degradation and noise, inherent gap between metric …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Implicit neural representations (INRs) have emerged as a powerful paradigm in scene
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …

Representing signals using coordinate networks dominates the area of inverse problems
recently and is widely applied in various scientific computing tasks. Still there exists an issue …

Intraoperative imaging techniques for reconstructing deformable tissues in vivo are pivotal
for advanced surgical systems. Existing methods either compromise on rendering quality or …

Implicit neural representations (INR) have gained significant popularity for signal and image
representation for many end-tasks, such as superresolution, 3D modeling, and more. Most …

Sparse-view Computed Tomography (SVCT) has great potential for decreasing patient
radiation exposure dose during scanning. In this work, we propose a Self-supervised …

Reconstructing models of the real world, including 3D geometry, appearance, and motion of
real scenes, is essential for computer graphics and computer vision. It enables the …

Parallel imaging is a commonly used technique to accelerate magnetic resonance imaging
(MRI) data acquisition. Mathematically, parallel MRI reconstruction can be formulated as an …

Diffusion models excel in solving imaging inverse problems due to their ability to model
complex image priors. However, their reliance on large, clean datasets for training limits …