Abstract
A tone mapping operator converts High Dynamic Range (HDR) images to Low Dynamic Range (LDR) images, which can be seen on LDR displays. There has been a lot of research done in the direction of an optimal Tone Mapping Operator which maximizes Tone Mapping Quality Index (TMQI). However, since all the methods approximate Human Vision System in one or different way, none of them works for every type of images. We are proposing a novel generative adversarial network to learn a combination of these tone mapping operators. In order to get pixel level accuracy, we are using residual connections between same sized network layers. We compare this method with some of the existing tone mapping operators and observe that our method generates images with comparably high TMQI and indeed works on many different types of images. Because of the residual connections, the network can be scaled to very high dimensional images.
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Acknowledgment
The authors would like to thank Maharshi Vyas for helping with python scripts and NVIDIA for TitanX GPU card grant.
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Patel, V.A., Shah, P., Raman, S. (2018). A Generative Adversarial Network for Tone Mapping HDR Images. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_20
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DOI: https://doi.org/10.1007/978-981-13-0020-2_20
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