Photometric stereo recovers the surface normals of an object from multiple images with
varying shading cues, ie, modeling the relationship between surface orientation and …

Evaluating photometric stereo using real-world dataset is important yet difficult. Existing
datasets are insufficient due to their limited scale and random distributions in shape and …

Recovering 3D surfaces based on the photometric stereo is a challenging task, due to the
non-Lambertian surface of real-world objects. Although much effort has been made to …

Multispectral photometric stereo (MPS) aims at recovering the surface normal of a scene
from a single-shot multispectral image captured under multispectral illuminations. Existing …

Recently, removing rain streaks from a single image has attracted a lot of attention because
rain streaks can severely degrade the perceptual quality of the image and cause many …

Digitalization techniques, such as photogrammetry (PG), are attracting the interest of experts
in the cultural heritage field, as they enable the creation of three-dimensional virtual replicas …

Deep learning-based models have achieved unprecedented performance in single image
super-resolution (SISR). However, existing deep learning-based models usually require …

Estimating pixel-wise surface normal from a single image is a challenging task but offers
great values to computer vision and robotics applications. By using the spectrally and …

Single image super-resolution (SISR) is a classic ill-posed problem in computer vision. In
recent years, deep-learning-based (DL-based) models have achieved promising results with …

Modern marine research requires high-precision three-dimensional (3D) underwater data.
Underwater environments experience severe visible light attenuation, which leads to inferior …