TL;DR
OverNet is a lightweight, multi-scale super-resolution network that efficiently handles arbitrary scale factors with a single model, outperforming previous methods in benchmarks while using fewer parameters.
Contribution
It introduces a recursive feature extractor, an overscaling reconstruction module, and a multi-scale loss for improved generalization and efficiency in super-resolution tasks.
Findings
Outperforms state-of-the-art in benchmarks
Uses fewer parameters than previous methods
Effective for arbitrary scale super-resolution
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…
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