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Image Interpolation Using Multi-scale Attention-aware Inception Network.
- Source :
-
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society [IEEE Trans Image Process] 2020 Sep 30; Vol. PP. Date of Electronic Publication: 2020 Sep 30. - Publication Year :
- 2020
- Publisher :
- Ahead of Print
-
Abstract
- A new multi-scale deep learning (MDL) framework is proposed and exploited for conducting image interpolation in this paper. The core of the framework is a seeding network that needs to be designed for the targeted task. For image interpolation, a novel attention-aware inception network (AIN) is developed as the seeding network; it has two key stages: 1) feature extraction based on the low-resolution input image; and 2) feature-to-image mapping to enlarge image's size or resolution. Note that the designed seeding network, AIN, needs to be trained with a matched training dataset at each scale. For that, multi-scale image patches are generated using our proposed pyramid cut, which outperforms the conventional image pyramid method by completely avoiding aliasing issue. After training, the trained AINs are then combined for processing the input image in the testing stage. Extensive experimental simulation results obtained from seven image datasets (comprising 359 images in total) have clearly shown that the proposed MAIN consistently delivers highly accurate interpolated images.
Details
- Language :
- English
- ISSN :
- 1941-0042
- Volume :
- PP
- Database :
- MEDLINE
- Journal :
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
- Publication Type :
- Academic Journal
- Accession number :
- 32997630
- Full Text :
- https://doi.org/10.1109/TIP.2020.3026632