Restaurando imagens com uma rede neural convolucional

Autores/as

  • Guilherme A. Lizarzaburu Escola Politécnica da USP
  • Renato Candido Escola Politécnica da USP
  • Magno T. M. Silva Escola Politécnica da USP

Resumen

Soluções para restauração de imagens com técnicas de aprendizado de máquina têm sido pro-postas na literatura [2,6,7,9].[...]

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Citas

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Bigdeli, S. A. and Zwicker, M. Image restoration using autoencoding priors,13th Int. JointConf. Comp. Vision, Imaging and Comp. Graphics Theory and Appl., 5: 33–44, 2018. DOI:10.5220/0006532100330044.

Goodfellow, I., Bengio, Y. and Courville, A.Deep Learning. MIT Press, 2016.[4] He, K., Zhang, X., Ren, S. and Sun, J. Deep residual learning for image recognition,IEEE Conf. Com-puter Vision and Pattern Recognition, 1: 770–778, 2016. DOI: 10.1109/CVPR.2016.90.[5] Kundur, D. and Hatzinakos, D. Blind image deconvolution,IEEE Signal Process. Mag., 13: 43–46,1996. DOI: 10.1109/79.489268.

Lizarzaburu, G. A., Candido, R. and Silva, M. T. M. Restaura ̧c ̃ao de imagens coloridas usandouma rede neural convoucional residual,Simp ́osio Brasileiro de Telecomunica ̧c ̃oes e Processamento deSinais, 2020. DOI: 10.14209/SBRT.2020.1570649730.

Mao, X., Chunhua, S. and Yu-Bin, Y. Image restoration using very deep convolutional enc.-dec.networks w/ symmetric skip connections,30th Int. Conf. on Neural Info. Processing Systems, 2810–2818, 2016. DOI: 10.5555/3157382.3157412.

Wang, Z., Bovik, A. C., Sheikh, H. R. and Simoncelli, E. P.Image quality assessment: fromerror visibility to structural similarity,IEEE Trans. Image Process., 13: 600–612, 2004. DOI:10.1109/TIP.2003.819861.

Zhao, H. Loss functions for image restoration with neural networks.IEEE Trans. ComputationalImaging, 3: 47–57, 2016. DOI: 10.1109/TCI.2016.2644865.

Publicado

2021-12-20

Número

Sección

Resumos