In real scenes, due to the problems of low light and unsuitable views, the images often
exhibit a variety of degradations, such as low contrast, color distortion, and noise. These …

Compositing an image usually inevitably suffers from inharmony problem that is mainly
caused by incompatibility of foreground and background from two different images with …

We show how to train a fully convolutional neural network to perform inverse rendering from
a single, uncontrolled image. The network takes an RGB image as input, regresses albedo …

We explore the zero-shot setting for day-night domain adaptation. The traditional domain
adaptation setting is to train on one domain and adapt to the target domain by exploiting …

Estimating the reflectance layer from a single image is a challenging task. It becomes more
challenging when the input image contains shadows or specular highlights, which often …

Intrinsic decomposition is a fundamental mid-level vision problem that plays a crucial role in
various inverse rendering and computational photography pipelines. Generating highly …

Intrinsic imaging or intrinsic image decomposition has traditionally been described as the
problem of decomposing an image into two layers: a reflectance, the albedo invariant color …

The waive of labels in the target domain makes Unsupervised Domain Adaptation (UDA) an
attractive technique in many real-world applications, though it also brings great challenges …

In this paper, an automated layer defect detection system for construction 3D printing is
proposed. Initially, a step-by-step procedure is implemented to develop a deep …

Intrinsic image decomposition is the process of recovering the image formation components
(reflectance and shading) from an image. Previous methods employ either explicit priors to …