scholarly journals Optimal Tone-Mapping for HDR Images

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3078-3080

This research paper proposes a unique optimal tone-mapping technique for high dynamic range (HDR) images, performing local adjustments with overlapping windows covering complete image. A local linear adjustment is applied on each window to preserve the radiance values. This problem may be treated as global optimization problems to satisfy the local restriction for every overlapping window. These Local constraints may be considered as a guidance map to suppress high contrast without losing its details. M-estimation technique may be used for solving this optimization problem. This technique may be applied to HDR images with sudden radiance changes or comparatively smooth transitions. Further, this technique may be applied to differentiate and analyzes HDR images from LDR images. Simulation results are included to support the performance gains achieved by the proposed technique.

2013 ◽  
Vol 397-400 ◽  
pp. 2318-2321
Author(s):  
Feng Qin Liu ◽  
Yi Guang Zhang ◽  
Zhen Chao ◽  
Peng Miao

Visualizing high dynamic range image or video on traditional display devices need fast tone mapping technique. Traditional bin-based histogram equalization is fast enough but cannot handle high dynamic range data, e.g. single or double precision. Other common tone mapping methods require a high computational complexity. In this paper, we propose a new continuous data equalization method based on simple sorting, which can quickly and efficiently enhance the visualization of high dynamic range data. This method can be applied to real-time visualization of high dynamic range video with cost effective hardware acceleration.


2009 ◽  
Vol 35 (2) ◽  
pp. 113-122 ◽  
Author(s):  
Ke-Hu YANG ◽  
Jing JI ◽  
Jian-Jun GUO ◽  
Wen-Sheng YU

2005 ◽  
Vol 24 (3) ◽  
pp. 640-648 ◽  
Author(s):  
Patrick Ledda ◽  
Alan Chalmers ◽  
Tom Troscianko ◽  
Helge Seetzen

2011 ◽  
Vol 6 (2) ◽  
pp. 283-295 ◽  
Author(s):  
Fabrizio Guerrini ◽  
Masahiro Okuda ◽  
Nicola Adami ◽  
Riccardo Leonardi

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