TMO-Net: A Parameter-Free Tone Mapping Operator Using Generative Adversarial Network, and Performance Benchmarking on Large Scale HDR Dataset

TMO-Net: A Parameter-Free Tone Mapping Operator Using Generative Adversarial Network, and Performance Benchmarking on Large Scale HDR Dataset

Currently published tone mapping operators (TMO) are often evaluated on a very limited test set of high dynamic range (HDR) images. Thus, the resulting performance index is highly subject to extensive hyperparameter tuning, and many TMOs exhibit sub-optimal performance when tested on a broader spect...

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Bibliographic Details
Main Authors: Karen Panetta, Landry Kezebou, Victor Oludare, Sos Agaian, Zehua Xia
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Deep learning
GAN-based tone mapping
TMO benchmarking
tone mapping
TMO-Net HDR dataset
parameter-free tone mapping
Online Access:https://ieeexplore.ieee.org/document/9371686/

Internet

https://ieeexplore.ieee.org/document/9371686/

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