The ultimate goal of neuroscience is to explain how complex behaviour arises from neuronal
activity. A comparable level of complexity also emerges in deep neural networks (DNNs) …

The intrinsic properties of the ambient illuminant significantly alter the true colors of objects
within an image. Most existing color constancy algorithms assume a uniformly lit scene …

As the basis of high-level visual tasks, edge detection is significant. Most of the encoder-
decoder edge detection methods used convolutional neural networks, such as VGG16 or …

Estimating the illuminant from a color-biased image is an ill-posed problem without prior
information or invariance about the surfaces of a scene. Based on the classical image …

The present work proposes to evaluate, compare, and determine software alternatives that
present good detection performance and low computational cost for the plant segmentation …

In this paper we present a deep learning method to estimate the illuminant of an image. Our
model is not trained with illuminant annotations, but with the objective of improving …

Color constancy is the capability to observe the true color of a scene from its image
regardless of the scene's illuminant. It is a significant part of the digital image processing …

Deep learning methods have achieved a large step forward in many computer vision
applications. With mechanisms such as attention, deep models can now guide themselves …

Color constancy is a basic step for achieving stable color perception in both biological visual
systems and the image signal processing (ISP) pipeline of cameras. So far, there have been …

Achieving color constancy is an important part of image preprocessing pipeline of
contemporary digital cameras. Its goal is to eliminate the influence of the illumination color …