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2020 | OriginalPaper | Buchkapitel

Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation

verfasst von : Xu Yan, Weibing Zhao, Kun Yuan, Ruimao Zhang, Zhen Li, Shuguang Cui

Erschienen in: Computer Vision – ECCV 2020

Verlag: Springer International Publishing

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Abstract

Recovering realistic textures from a largely down-sampled low resolution (LR) image with complicated patterns is a challenging problem in image super-resolution. This work investigates a novel multi-reference based super-resolution problem by proposing a Content Independent Multi-Reference Super-Resolution (CIMR-SR) model, which is able to adaptively match the visual pattern between references and target image in the low resolution and enhance the feature representation of the target image in the higher resolution. CIMR-SR significantly improves the flexibility of the recently proposed reference-based super-resolution (RefSR), which needs to select the specific high-resolution reference (e.g., content similarity, camera view and relative scale) for each target image. In practice, a universal reference pool (RP) is built up for recovering all LR targets by searching the local matched patterns. By exploiting feature-based patch searching and attentive reference feature aggregation, the proposed CIMR-SR generates realistic images with much better perceptual quality and richer fine-details. Extensive experiments demonstrate the proposed CIMR-SR outperforms state-of-the-art methods in both qualitative and quantitative reconstructions.

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Metadaten
Titel
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation
verfasst von
Xu Yan
Weibing Zhao
Kun Yuan
Ruimao Zhang
Zhen Li
Shuguang Cui
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-58595-2_4