Contrast Sensitivity Based Multiscale Base–Detail Separation for Enhanced HDR Imaging
Source abstract
Abstract
High dynamic range (HDR) imaging is used to represent scenes with a greater dynamic range of luminance on a standard dynamic range display. Usually, HDR images are synthesized through base–detail separations. The base layer is used for tone compression and the detail layer is used for detail preservation. The representative detail-preserved algorithm iCAM06 has a tendency to reduce the sharpness of dim surround images, because of the fixed edge-stopping function of the fast-bilateral filter (FBF). This paper proposes a novel base–detail separation and detail compensation technique using the contrast sensitivity function (CSF) in the segmented frequency domain. Experimental results show that the proposed rendering method has better sharpness features and image quality than previous methods correlated by the human visual system.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image Enhancement Techniques · Color Science and Applications · Computer Graphics and Visualization Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronics Engineering, Kyungpook National University, 80 Daehak-ro, Buk-Gu, Daegu 41566, Korea
Funding
National Research Foundation of Korea
References (22)
Show source-provided references
1. Fairchild, M.D. (2013). Color Appearance Models, Wiley-IS&T. [3rd ed.].10.1002/9781118653128 ↗
2. iCAM06: A refined image appearance model for HDR image rendering10.1016/j.jvcir.2007.06.003 ↗
3. Edge-preserving decompositions for multi-scale tone and detail manipulation10.1145/1360612.1360666 ↗
4. Fast bilateral filtering for the display of high-dynamic-range images10.1145/566654.566574 ↗
5. High dynamic range image rendering with a retinex-based adaptive filter10.1109/TIP.2006.877312 ↗
6. Enhanced high dynamic-range image rendering using a surround map based on edge-adaptive layer blurring10.1049/iet-cvi.2015.0160 ↗
7. Calibrated image appearance reproduction10.1145/2366145.2366220 ↗
8. A tone compression model for the compensation of white point shift generated from HDR rendering10.1587/transfun.E95.A.1297 ↗
9. Compensation of de-saturation effect in HDR imaging using a real scene adaptation model10.1016/j.jvcir.2012.03.001 ↗
10. CAM-based HDR image reproduction using CA–TC decoupled JCh decomposition10.1016/j.image.2018.08.010 ↗
11. Evaluation of tone mapping operators using a high dynamic range display10.1145/1073204.1073242 ↗
12. Visual sensitivity correlated tone reproduction for low dynamic range images in the compression field10.1117/1.OE.53.11.113111 ↗
13. Luminance adaptation transform based on brightness functions for LDR image reproduction10.1016/j.dsp.2014.03.008 ↗
14. Brightness Perception in Complex Fields10.1364/JOSA.57.000953 ↗
15. Model of luminance contrast-sensitivity function for application to image assessment10.1002/col.20230 ↗
16. Liang, Z., Xu, J., Zhang, D., Cao, Z., and Zhang, L. (2018, January 18–22). A hybrid l1-l0 layer decomposition model for tone mapping. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00500 ↗
17. S3: A Spectral and Spatial Measure of Local Perceived Sharpness in Natural Images10.1109/TIP.2011.2169974 ↗
18. Narvekar, N.D., and Karam, L.J. (2009, January 29–31). A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection. Proceedings of the 2009 International Workshop on Quality of Multimedia Experience, San Diego, CA, USA.10.1109/QOMEX.2009.5246972 ↗
19. Image sharpness assessment based on local phase coherence10.1109/TIP.2013.2251643 ↗
20. A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)10.1109/TIP.2008.2011760 ↗
21. Objective quality assessment of tone-mapped images10.1109/TIP.2012.2221725 ↗
22. Venkatanath, N., Praneeth, D., Bh, M.C., Channappayya, S.S., and Medasani, S.S. (2015, January 1). Blind image quality evaluation using perception based features. Proceedings of the 2015 Twenty First National Conference on Communications (NCC), Mumbai, India.10.1109/NCC.2015.7084843 ↗