50 papers with code • 3 benchmarks • 6 datasets
Remove shadow from background
This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples.
Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal
Specifically, a shadow image is fed into the first generator which produces a shadow detection mask.
With the help of novel masks or scenes, we enhance the current datasets using synthesized shadow images.
To our best knowledge, we are the first one to explore residual and illumination for shadow removal.
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.
In this paper, we propose a novel algorithm to rectify illumination of the digitized documents by eliminating shading artifacts.
For taking advantage of specific properties of document images, a background estimation module is designed for extracting the global background color of the document.
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.
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.