TY - GEN
T1 - Image fusion-based tone mapping using gaussian mixture model clustering
AU - Lee, Wang Un
AU - Park, Seung
AU - Ko, Sung Jea
N1 - Funding Information:
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2019-0-00268, Development of SW technology for recognition, judgement and path control algorithm verification simulation and dataset generation)
Publisher Copyright:
© 2020 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - Tone mapping (TM) algorithms convert high dynamic range (HDR) images into low dynamic range (LDR) images to represent on conventional display devices. Most TM methods compress the dynamic range of input HDR images by using a global transformation function (TF), and then improve local detail by applying contrast enhancement techniques. However, these approaches often fail to restore local detail lost in the dynamic range compression. To solve this problem, we propose a novel image fusion-based TM method. We use Gaussian mixture model clustering algorithm to estimate the dark and bright distributions in the luminance histogram of the input HDR image. Then, we generate two LDR images using two locally-adaptive TFs obtained by the components of each distribution. Finally, the output image is produced by the image fusion technique employing a brightness weight and a local contrast weight. The experimental results show that the proposed algorithm achieves high performance compared to state-of-the-art methods in terms of detail preservation and brightness adjustment.
AB - Tone mapping (TM) algorithms convert high dynamic range (HDR) images into low dynamic range (LDR) images to represent on conventional display devices. Most TM methods compress the dynamic range of input HDR images by using a global transformation function (TF), and then improve local detail by applying contrast enhancement techniques. However, these approaches often fail to restore local detail lost in the dynamic range compression. To solve this problem, we propose a novel image fusion-based TM method. We use Gaussian mixture model clustering algorithm to estimate the dark and bright distributions in the luminance histogram of the input HDR image. Then, we generate two LDR images using two locally-adaptive TFs obtained by the components of each distribution. Finally, the output image is produced by the image fusion technique employing a brightness weight and a local contrast weight. The experimental results show that the proposed algorithm achieves high performance compared to state-of-the-art methods in terms of detail preservation and brightness adjustment.
KW - GMM clustering
KW - High dynamic range imaging
KW - Tone mapping
UR - http://www.scopus.com/inward/record.url?scp=85082566250&partnerID=8YFLogxK
U2 - 10.1109/ICCE46568.2020.9042964
DO - 10.1109/ICCE46568.2020.9042964
M3 - Conference contribution
AN - SCOPUS:85082566250
T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics
BT - 2020 IEEE International Conference on Consumer Electronics, ICCE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Consumer Electronics, ICCE 2020
Y2 - 4 January 2020 through 6 January 2020
ER -