UCAN: A Learning-based Model to Enhance Poorly Exposed Images
Resumo
Image enhancement is a critical process in imagebased systems. In these systems, image quality is a crucial factor to achieve a good performance. Scenes with a dynamic range above the capability of the camera or poor lighting are challenging conditions, which usually result in low contrast images, and, with that, we can have the underexposure and/or overexposure problem. In this work, our aim is to restore illexposed images. For this purpose, we present UCAN, a small and fast learning-based model capable to restore and enhance poorly exposed images. The obtained results are evaluated using image quality indicators which show that the proposed network is able to improve images damaged by real and simulated exposure. Qualitative and quantitative results show that the proposed model outperforms the existing models for this objective.Referências
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A. B. Petro, C. Sbert, and J.-M. Morel, "Multiscale retinex," Image Processing On Line, pp. 71–88, 2014.
Z. Ying, G. Li, Y. Ren, R. Wang, and W. Wang, "A new low-light image enhancement algorithm using camera response model," in Computer Vision Workshop (ICCVW), 2017 IEEE International Conference on. IEEE, 2017, pp. 3015–3022.
——, "A new image contrast enhancement algorithm using exposure fusion framework," in International Conference on Computer Analysis of Images and Patterns. Springer, 2017, pp. 36–46.
T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro, "High-resolution image synthesis and semantic manipulation with conditional gans," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 8798–8807.
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Q. Chen, J. Xu, and V. Koltun, "Fast image processing with fully-convolutional networks," in IEEE International Conference on Computer Vision, vol. 9, 2017, pp. 2516–2525.
C. Chen, Q. Chen, J. Xu, and V. Koltun, "Learning to see in the dark," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
J. Cai, S. Gu, and L. Zhang, "Learning a deep single image contrast enhancer from multi-exposure images," IEEE Transactions on Image Processing, vol. 27, no. 4, pp. 2049–2062, 2018.
C. Li, J. Guo, F. Porikli, and Y. Pang, "Lightennet: a convolutional neural network for weakly illuminated image enhancement," Pattern Recognition Letters, vol. 104, pp. 15–22, 2018.
O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in International Conference on Medical image computing and computer-assisted intervention. Springer, 2015, pp. 234–241.
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Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE transactions on image processing, vol. 13, no. 4, pp. 600–612, 2004.
X. Fu, Y. Liao, D. Zeng, Y. Huang, X.-P. Zhang, and X. Ding, "A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation," IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 4965–4977, 2015.
R. Szeliski, Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
V. Bychkovsky, S. Paris, E. Chan, and F. Durand, "Learning photo-graphic global tonal adjustment with a database of input / output image pairs," in The Twenty-Fourth IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
S. W. Hasinoff, D. Sharlet, R. Geiss, A. Adams, J. T. Barron, F. Kainz, J. Chen, and M. Levoy, "Burst photography for high dynamic range and low-light imaging on mobile cameras," ACM Transactions on Graphics (TOG), vol. 35, no. 6, p. 192, 2016.
C. R. Steffens, L. R. V. Messias, P. J. L. Drews-Jr, and S. S. d. C. Botelho, "Cnn based image restoration," Journal of Intelligent & Robotic Systems, vol. 99, no. 3, pp. 609–627, Sep 2020. [Online]. Available: https://doi.org/10.1007/s10846-019-01124-9
T. Mertens, J. Kautz, and F. Van Reeth, "Exposure fusion," in Computer Graphics and Applications, 2007. PG’07. 15th Pacific Conference on. IEEE, 2007, pp. 382–390.
D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014.
Publicado
07/11/2020
Como Citar
MESSIAS, Lucas R. V.; STEFFENS, Cristiano R.; DREWS-JR, Paulo L. J.; BOTELHO, Silvia S. C..
UCAN: A Learning-based Model to Enhance Poorly Exposed Images. In: WORKSHOP DE TRABALHOS DA GRADUAÇÃO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 33. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 171-174.
DOI: https://doi.org/10.5753/sibgrapi.est.2020.13004.
