Abstract
Visual functions of the elderly are gradually changing with age. As one of the changes, aged eyes have a different property in perceiving high-frequency components from younger eyes. In general, the elderly perceives images differently from the younger. To give the same perception for the same image, a different image from an original image needs to be displayed to the elderly. In this paper, a method of generating the inverse characteristic of the image filter for the presbyopia is proposed. To this end, a serially-cascaded neural network model is proposed. The neural network is composed of 4 layers. The 4-layer neural network is divided into 2 blocks on the aspect of function. The upper and the lower layers play a role of simulating the image conversion and obtaining its opposite characteristic, respectively. The performance of the proposed framework is evaluated by the experiments on the image pre-conversion for the presbyopia.
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Acknowledgments
This work is partially supported by JSPS KAKENHI Grant Number 25730138. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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Kawano, H., Hayashi, K., Orii, H., Maeda, H. (2015). Prior Image Transformation for Presbyopia Employing Serially-Cascaded Neural Network. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_65
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DOI: https://doi.org/10.1007/978-3-319-26555-1_65
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