The proposed approach is applied to superresolution in two scenarios: a neural network that departs from the bicu- bic upsampling of the input image (SRCNN [3]) ...
For most pixel-processing tasks, rigid transformations should commute with the processing: a rigid transformation of the input should result in that same ...
Missing: Super Resolution.
Stationarity of reconstruction problems is the crux to en- abling convolutional neural networks for many image pro- cessing tasks: the output estimate for a ...
Missing: Super Resolution.
Exploiting Reflectional and Rotational Invariance in Single Image ...
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Because of the encompassing nature of the proposed architecture, it can directly enhance existing CNN-based algorithms. We show how it can be applied to SRCNN ...
Missing: Super Resolution.
This paper proposes super-resolution reconstruction algorithm for image enhancement. Super-resolution reconstruction algorithms reconstruct a high-resolution ...
Exploiting Reflectional and Rotational Invariance in Single Image Superresolution. Simon Donne,. Laurens Meeus,. Hiep Quang Luong,. Bart Goossens,. Wilfried ...
2015. Exploiting reflectional and rotational invariance in single image superresolution. S Donne, L Meeus, H Quang Luong, B Goossens, W Philips. Proceedings of ...
Exploiting Reflectional and Rotational Invariance in Single Image Superresolution, Donn, Simon; Meeus, Laurens; Luong, Hiep Quang; Goossens, Bart; Philips ...
Fast and robust variational optical flow for high-resolution images ... Exploiting reflectional and rotational invariance in single image superresolution.
FormResNet: Formatted Residual Learning for Image Restoration pp. 1034-1042. Exploiting Reflectional and Rotational Invariance in Single Image Superresolution ...