Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79650
Title: Clustering based content and color adaptive tone mapping
Authors: Li, H 
Jia, XX 
Zhang, L 
Keywords: High dynamic range
Tone mapping
Clustering
Issue Date: 2018
Publisher: Academic Press
Source: Computer vision and image understanding, Mar. 2018, v. 168, special issue, p. 37-49 How to cite?
Journal: Computer vision and image understanding 
Abstract: By extracting image luminance channel and separating it into a base layer and a detail layer, the Retinex theory has been widely adopted for tone mapping to visualize high dynamic range (HDR) images on low dynamic range display devices. Many edge-preservation filtering techniques have been proposed to approximate the base layer for Retinex image decomposition; however, the associated tone mapping methods are prone to halo artifacts and false colors because filtering methods are limited in adapting the complex image local structures. We present a statistical clustering based tone mapping method which can more faithfully adapt image local content and colors. We decompose each color patch of the HDR image into three components, patch mean, color variation and color structure, and cluster the patches into a number of clusters. For each cluster, an adaptive subspace can be easily learned by principal component analysis, via which the patches are transformed into a more compact domain for effective tone mapping. Comparing with the popular edge-preservation filtering methods, the proposed clustering based method can better adapt to image local structures and colors by exploiting the image global redundancy. Our experimental results demonstrate that it can produce high-quality image with well-preserved local contrast and vivid color appearance. Furthermore, the proposed method can be extended to multi-scale for more faithful texture preservation, and off-line subspace learning for efficient implementation.
URI: http://hdl.handle.net/10397/79650
ISSN: 1077-3142
DOI: 10.1016/j.cviu.2017.11.001
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

3
Citations as of Jan 5, 2019

WEB OF SCIENCETM
Citations

2
Citations as of Jan 14, 2019

Page view(s)

1
Citations as of Jan 14, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.