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

• Published in: Proceedings / IEEE Workshop on Applications of Computer Vision pp. 2693 - 2702
• Main Authors: Behjati, Parichehr, Rodriguez, Pau, Mehri, Armin, Hupont, Isabelle, Tena, Carles Fernandez, Gonzalez, Jordi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2021
• Subjects: Computational modeling; Data mining; Feature extraction; Memory management; Noise reduction; Superresolution; Training
• ISSN: 2642-9381
• DOI: 10.1109/WACV48630.2021.00274

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.