20 papers with code • 1 benchmarks • 1 datasets
With the help of novel masks or scenes, we enhance the current datasets using synthesized shadow images.
This paper presents a novel deep neural network design for shadow detection and removal by analyzing the spatial image context in a direction-aware manner.
This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples.
We conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate our method's effectiveness and show better performance in shadow regions and comparable performance in non-shadow regions over the state-of-the-art methods.
To overcome this challenge, we present SynShadow, a novel large-scale synthetic shadow/shadow-free/matte image triplets dataset and a pipeline to synthesize it.
Inspired by physical models of shadow formation, we use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte layer.
Ranked #1 on Shadow Removal on Adjusted ISTD
Non-uniform and multi-illuminant color constancy are important tasks, the solution of which will allow to discard information about lighting conditions in the image.
A user-centric method for fast, interactive, robust and high-quality shadow removal is presented.
In this paper, we propose a novel algorithm to rectify illumination of the digitized documents by eliminating shading artifacts.