Paper Info
Title
Learning To Generate Multi-Exposure Stacks With Cycle Consistency For High Dynamic Range Imaging
Abstract
Inverse tone mapping aims at recovering the lost scene radiances from a single exposure image. With the successful use of deep learning in numerous applications, many inverse tone mapping methods use convolution neural networks in a supervised manner. As these approaches are trained with many pre-fixed high dynamic range (HDR) images, they fail to flexibly expand the dynamic ranges of images. To overcome this limitation, we consider a multiple exposure image synthesis approach for HDR imaging. In particular, we propose a pair of neural networks that represent mappings between images that have exposure levels one unit apart (stop-up/down network). Therefore, it is possible to construct two positive-feedback systems to generate images with greater or lesser exposure. Compared to previous works using the conditional generative adversarial learning framework, the stop-up/down network employs HDR friendly network structures and several techniques to stabilize the training processes. Experiments on HDR datasets demonstrate the advantages of the proposed method compared to conventional methods. Consequently, we apply our approach to restore the full dynamic range of scenes agilely with only two networks and generate photorealistic images in complex lighting situations.
Year
DOI
Venue
2021
10.1109/TMM.2020.3013378
IEEE TRANSACTIONS ON MULTIMEDIA
Keywords
DocType
Volume
Dynamic range, Neural networks, Image restoration, Distortion, Training, Light sources, Brightness, High dynamic range imaging, inverse-tone mapping, image restoration, deep learning
Journal
23
ISSN
Citations 
PageRank 
1520-9210
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Siyeong Lee173.16
So Yeon Jo200.34
Gwon Hwan An321.71
Suk-Ju Kang412727.68