OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network

• URL: http://arxiv.org/abs/2008.02382v2
• Date: Mon, 9 Nov 2020 17:11:58 GMT
• Title: OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network
• Authors: Parichehr Behjati, Pau Rodriguez, Armin Mehri, Isabelle Hupont, Jordi Gonzalez, Carles Fernandez Tena
• Abstract summary: We introduce OverNet, a deep but lightweight convolutional network to solve SISR at arbitrary scale factors with a single model. We show that our network outperforms previous state-of-the-art results in standard benchmarks while using fewer parameters than previous approaches.
• Score: 3.6683231417848283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Moreover, most of them 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 recursive 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 reconstruction module that generates accurate high-resolution images from overscaled feature maps and can be independently used to improve existing architectures. Third, we introduce a multi-scale loss function to achieve generalization across scales. Through extensive experiments, we demonstrate that our network outperforms previous state-of-the-art results in standard benchmarks while using fewer parameters than previous approaches.

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