Details

Autor(en) / Beteiligte

• Yang, Wenhan
• Liu, Jiaying
• Xia, Sifeng
• Guo, Zongming

Titel

Variation learning guided convolutional network for image interpolation

Ist Teil von

• 2017 IEEE International Conference on Image Processing (ICIP), 2017, p.1652-1656

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• In this paper, we propose a variational learning model that effectively exploits the structural similarities for image representation, and construct a deep network based on this model for image interpolation. Based on the local dependency, our learning model represents an image as the three-dimensional features. Besides two coordinate dimensions, an additional neighboring variation dimension is added to encode every pixel as the variation to its nearest low-resolution pixel by the local similarity. This added dimension lowers the risk of over-fitting for learning approaches and constructs abundant structural correspondences for inferring the missing information lost in image degradation. Then, this three-dimensional features are naturally modeled, extracted and refined by an end-to-end trainable recurrent convolutional network for image interpolation. Comprehensive experiments demonstrate that our method leads to a surprisingly superior performance and offers new state-of-the-art benchmark.