Inception learning super-resolution.
Appl Opt
; 56(22): 6043-6048, 2017 Aug 01.
Article
em En
| MEDLINE
| ID: mdl-29047798
ABSTRACT
An efficient network for super-resolution, which we refer to as inception learning super-resolution (ILSR), is proposed. We adopt the inception module from GoogLeNet to exploit multiple features from low-resolution images, yet maintain fast training steps. The proposed ILSR network demonstrates low computation time and fast convergence during the training process. It is divided into three parts feature extraction, mapping, and reconstruction. In feature extraction, we apply the inception module followed by dimensional reduction. Then, we map features using a simple convolutional layer. Finally, we reconstruct the high-resolution component using the inception module and a 1×1 convolutional layer. Experimental results demonstrate that the proposed network can construct sharp edges and clean textures, and reduce computation time by up to three orders of magnitude compared to state-of-the-art methods.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article