Boosting Single Image Super-Resolution Learnt From Implicit Multi-Image Prior

Dingjian Jin

doi:10.1109/TIP.2021.3059507

Boosting Single Image Super-Resolution Learnt From Implicit Multi-Image Prior

2021-01-01

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DOI：10.1109/TIP.2021.3059507

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Learning-based single image super-resolution (SISR) aims to learn a versatile mapping from low resolution (LR) image to its high resolution (HR) version. The critical challenge is to bias the network training towards continuous and sharp edges. For the first time in this work, we propose an implicit boundary prior learnt from multi-view observations to significantly mitigate the challenge in SISR we outline. Specifically, the multi-image prior that encodes both disparity information and boundary structure of the scene supervise a SISR network for edge-preserving. For simplicity, in the training procedure of our framework, light field (LF) serves as an effective multi-image prior, and a hybrid loss function jointly considers the content, structure, variance as well as disparity information from 4D LF data. Consequently, for inference, such a general training scheme boosts the performance of various SISR networks, especially for the regions along edges. Extensive experiments on representative backbone SISR architectures constantly show the effectiveness of the proposed method, leading to around 0.6 dB gain without modifying the network architecture.

Keywords:

Single image super resolution

multi-view data

light field

convolutional neural networks

Boosting Single Image Super-Resolution Learnt From Implicit Multi-Image Prior

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