OverNet: Lightweight multi-scale super-resolution with overscaling network

Parichehr Behjati*, Pau Rodriguez, Armin Mehri, Isabelle Hupont, Carles Fernandez Tena, Jordi Gonzalez

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)


Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the depth and width of the networks increase, CNN-based SR methods have been faced with the challenge of computational complexity in practice. More-over, most SR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. To address these limitations we introduce OverNet, a deep but lightweight convolutional network to solve SISR at arbitrary scale factors with a single model. We make the following contributions: first, we introduce a lightweight feature extractor that enforces efficient reuse of information through a novel recursive structure of skip and dense connections. Second, to maximize the performance of the feature extractor, we propose a model agnostic reconstruction module that generates accurate high-resolution images from overscaled feature maps obtained from any SR architecture. Third, we introduce a multi-scale loss function to achieve generalization across scales. Experiments show that our proposal outperforms previous state-of-the-art approaches in standard benchmarks, while maintaining relatively low computation and memory requirements.

Original languageEnglish
Pages (from-to)2693-2702
Number of pages10
JournalProceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
Volume(GGS Rating A, Class 2)
Publication statusPublished - Jan 2021


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