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Image Interpolation Using Multi-Scale Attention-Aware Inception Network
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DOI：10.1109/TIP.2020.3026632.png)
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Abstract
En 中文
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.
Keywords:
Interpolation
Task analysis
Machine learning
Image edge detection
Training
Image resolution
Image interpolation
image super-resolution
deep learning
multi-scale
convolutional neural network
attention-aware
inception network
image pyramid
pyramid cut
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