Version 1
: Received: 25 February 2023 / Approved: 28 February 2023 / Online: 28 February 2023 (01:24:14 CET)
How to cite:
Sethu, S.; Devaraj, J.; Wang, D. A Comprehensive Review of Deep Learning based Illumination Estimation. Preprints2023, 2023020478. https://doi.org/10.20944/preprints202302.0478.v1
Sethu, S.; Devaraj, J.; Wang, D. A Comprehensive Review of Deep Learning based Illumination Estimation. Preprints 2023, 2023020478. https://doi.org/10.20944/preprints202302.0478.v1
Sethu, S.; Devaraj, J.; Wang, D. A Comprehensive Review of Deep Learning based Illumination Estimation. Preprints2023, 2023020478. https://doi.org/10.20944/preprints202302.0478.v1
APA Style
Sethu, S., Devaraj, J., & Wang, D. (2023). A Comprehensive Review of Deep Learning based Illumination Estimation. Preprints. https://doi.org/10.20944/preprints202302.0478.v1
Chicago/Turabian Style
Sethu, S., Jayanthi Devaraj and Dongyi Wang. 2023 "A Comprehensive Review of Deep Learning based Illumination Estimation" Preprints. https://doi.org/10.20944/preprints202302.0478.v1
Abstract
Deep learning (DL) models have been recently widely used to extract task-oriented patterns from large scale of datasets, and to improve the data image understanding and analysis accuracy in many different decision-making processes for tasks such as image classification, segmentation, detection, and so on. However, in practice, the performances of DL models are easily affected by environmental illumination conditions. Conversely, DL models can also be utilized for extracting the illumination hints from the images, and these hints are critically useful for improving the model robustness, classifying the environmental scenes, estimating scene depth information, and rendering 3D objects. In this study, an extensive and exhaustive review is carried out for DL based color constancy, indoor and outdoor illumination estimation, and image depth estimations with the considerations of strengths and weaknesses of DL models. This study also explores the different network designs and the paradoxes in parameter optimization during the model training. Current technology barriers involved in implementing these models and recommendations to overcome these barriers are also suggested in the review.
Keywords
data preprocessing; feature extraction; deep learning; illumination estimation; color constancy
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
