This paper examines the problem of illumination spectra estimation in multispectral images.
We cast the problem into a constrained matrix factorization problem and present a method …

Hyperspectral (HS) satellite data have been widely applied in many fields due to its
numerous bands. Along with the advantages of high spectral resolution, HS satellite data …

The scarcity and low spatial resolution of hyperspectral images (HSIs) have become a major
problem limiting the application of the images. In recent years, spectral reconstruction (SR) …

Quantized low-precision neural networks are very popular because they require less
computational resources for inference and can provide high performance, which is vital for …

Multispectral (MS) imaging systems have a wide range of applications for computer vision
and computational photography tasks, but do not yet enjoy widespread adoption due to their …

Multispectral imaging is a technique that captures data across several bands of the light
spectrum, and it can be useful in many computer vision fields, including color constancy. We …

This paper introduces a novel framework for estimating the spectral power distribution of
daylight illuminants in uncalibrated hyperspectral images, particularly beneficial for drone …

This paper proposes to utilize deep spatio-spectral features for color constancy, while most
conventional methods focus on only spatial information of RGB images. We propose a novel …

We introduce a method that enhances RGB color constancy accuracy by combining neural
network and k-means clustering techniques. Our approach stands out from previous works …

This paper explores the problem of the estimation of illumination spectral power distribution
(SPD) derived both from sRGB images and a machine learning technique based on a vector …