Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation
In the image processing pipeline of almost every digital camera, there is a part for removing the influence of illumination on the colors of the image scene. Tuning the parameter values of an illumination estimation method for maximal accuracy requires calibrated images with known ground-truth illum...
Main Authors: | Nikola Banić, Sven Lončarić |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-10-01
|
Series: | Journal of Imaging |
Subjects: | |
Online Access: | https://www.mdpi.com/2313-433X/4/11/127 |
Similar Items
-
Color Constancy Adjustment Using Sub-Blocks of the Image
by: Md Akmol Hussain, et al.
Published: (2018-01-01) -
Iterative Convolutional Neural Network-Based Illumination Estimation
by: Karlo Koscevic, et al.
Published: (2021-01-01) -
Deep Learning-Based Illumination Estimation Using Light Source Classification
by: Karlo Koscevic, et al.
Published: (2020-01-01) -
Framework for Illumination Estimation and Segmentation in Multi-Illuminant Scenes
by: Donik Vrsnak, et al.
Published: (2023-01-01) -
N-White Balancing: White Balancing for Multiple Illuminants Including Non-Uniform Illumination
by: Teruaki Akazawa, et al.
Published: (2022-01-01)