Paper Info
Title
High Dynamic Range and Super-Resolution Imaging From a Single Image.
Abstract
This paper presents an algorithm for high dynamic range (HDR) and super-resolution (SR) imaging from a single image. First, we propose a new single image HDR imaging (HDRI) method based on the Retinex approach and exploit a recent single image SR method based on a convolutional neural network (CNN). Among many possible configurations of HDR and SR, we find an optimal system configuration and color manipulation strategy from the extensive experiments. Specifically, the best results are obtained when we first process the luminance component (Y) of input with our single image HDRI algorithm and then feed the enhanced HDR luminance to the CNN-based SR architecture that is trained by only luminance component. The ranges of chromatic components (U and V) are just scaled in proportion to the enhanced HDR luminance, and then they are bicubic interpolated or fed to the above CNN-based SR. Subjective and objective assessments for various experiments are presented to validate the effectiveness of the proposed HDR/SR imaging scheme.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2797197
IEEE ACCESS
Keywords
Field
DocType
Image enhancement,high dynamic range imaging,super resolution,convolutional neural network
Computer vision,Color constancy,Dynamic range,Computer science,HDRi,Convolutional neural network,Bicubic interpolation,Artificial intelligence,Luminance,High dynamic range,Image resolution,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Jae Sung Park1196.85
Jae Woong Soh2266.76
Nam Ik Cho3712106.98