Recent Developments on Super-Resolution

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Presentation transcript:

Recent Developments on Super-Resolution Yi-Wen Chen 2018.12.28

Super-Resolution Goal Application Reconstruct a high-resolution (HR) image from a low-resolution (LR) input image Application Video surveillance Medical diagnosis Remote sensing

CNN-based SR SRCNN [Dong et al. ECCV’14] VDSR [Kim et al. CVPR’16] first CNN-based SR VDSR [Kim et al. CVPR’16] increase the network depth ESPCN [Shi et al. CVPR’16] bicubic upsampling → sub-pixel convolution FSRCNN [Dong et al. ECCV’16] bicubic upsampling → transposed convolution

Bicubic interpolation SRCNN Ground Truth Bicubic interpolation Convolutional layer C. Dong, C. C. Loy, K. He, and X. Tang. Learning a deep convolutional network for image super-resolution. In ECCV, 2014.

SRCNN → VDSR Context Convergence Scale Factor deeper network (3 layers → 20 layers) → larger receptive field Convergence higher learning rate enabled by residual-learning and gradient clipping (training time: 3 days → 4 hours) Scale Factor cope with multiscale SR problem in a single network

Bicubic interpolation VDSR Bicubic interpolation J. Kim, J. K. Lee, and K. M. Lee. Accurate image super-resolution using very deep convolutional networks. In CVPR, 2016.

VDSR J. Kim, J. K. Lee, and K. M. Lee. Accurate image super-resolution using very deep convolutional networks. In CVPR, 2016.

ESPCN W. Shi, J. Caballero, F. Huszar, J. Totz, A. Aitken, R. Bishop, D. Rueckert, and Z. Wang. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In CVPR, 2016.

ESPCN W. Shi, J. Caballero, F. Huszar, J. Totz, A. Aitken, R. Bishop, D. Rueckert, and Z. Wang. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In CVPR, 2016.

FSRCNN C. Dong, C. C. Loy, and X. Tang. Accelerating the super-resolution convolutional neural network. In ECCV, 2016.

FSRCNN C. Dong, C. C. Loy, and X. Tang. Accelerating the super-resolution convolutional neural network. In ECCV, 2016.

References C. Dong, C. C. Loy, K. He, and X. Tang. Learning a deep convolutional network for image super-resolution. In ECCV, 2014. J. Kim, J. K. Lee, and K. M. Lee. Accurate image super- resolution using very deep convolutional networks. In CVPR, 2016. W. Shi, J. Caballero, F. Huszar, J. Totz, A. Aitken, R. Bishop, D. Rueckert, and Z. Wang. Real-time single image and video super-resolution using an efficient sub- pixel convolutional neural network. In CVPR, 2016. C. Dong, C. C. Loy, and X. Tang. Accelerating the super- resolution convolutional neural network. In ECCV, 2016.