TY - JOUR
T1 - An optimization framework for inverse tone mapping using a single low dynamic range image
AU - Fan, Ming
AU - Lee, Dae Hong
AU - Kim, Seung Wook
AU - Ko, Sung Jea
N1 - Funding Information:
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) ( 2017-0-00250 , Intelligent Defense Boundary Surveillance Technology Using Collaborative Reinforced Learning of Embedded Edge Camera and Image Analysis).
PY - 2019/10
Y1 - 2019/10
N2 - Conventional inverse tone-mapping (ITM) methods tend to produce contrast distortions such as contrast loss and contrast reversal in reconstructed high dynamic range (HDR) images. This paper proposes a novel ITM optimization framework based on the assumption that the input low dynamic range (LDR) image is similar to the LDR image obtained by tone mapping a true HDR image. In the proposed framework, an HDR image is initially reconstructed by applying a conventional tone-mapping function in a reverse manner, and then the reconstructed HDR image is iteratively modified toward the optimum HDR image by minimizing the difference between the input LDR image and a tone-mapped LDR image obtained from the reconstructed HDR image. The experimental results demonstrate that the proposed framework effectively reconstructs a high-quality HDR image and outperforms other conventional methods in terms of objective quality.
AB - Conventional inverse tone-mapping (ITM) methods tend to produce contrast distortions such as contrast loss and contrast reversal in reconstructed high dynamic range (HDR) images. This paper proposes a novel ITM optimization framework based on the assumption that the input low dynamic range (LDR) image is similar to the LDR image obtained by tone mapping a true HDR image. In the proposed framework, an HDR image is initially reconstructed by applying a conventional tone-mapping function in a reverse manner, and then the reconstructed HDR image is iteratively modified toward the optimum HDR image by minimizing the difference between the input LDR image and a tone-mapped LDR image obtained from the reconstructed HDR image. The experimental results demonstrate that the proposed framework effectively reconstructs a high-quality HDR image and outperforms other conventional methods in terms of objective quality.
KW - Brightness enhancement function
KW - Inverse tone mapping
KW - Newton–Raphson
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U2 - 10.1016/j.image.2019.07.009
DO - 10.1016/j.image.2019.07.009
M3 - Article
AN - SCOPUS:85069839917
VL - 78
SP - 274
EP - 283
JO - Signal Processing: Image Communication
JF - Signal Processing: Image Communication
SN - 0923-5965
ER -