A Deep Journey into Super-resolution: A survey
Saeed Anwar, Salman Khan, Nick Barnes

TL;DR
This survey comprehensively reviews over 30 deep learning-based super-resolution CNNs, comparing their architectures, performance, and challenges across multiple datasets, highlighting rapid progress and future research directions.
Contribution
It introduces a taxonomy of super-resolution networks, benchmarks numerous models, and analyzes architectural and performance differences in depth.
Findings
Deep learning models have significantly improved super-resolution accuracy.
Current models are more complex and require more resources.
Benchmark methods have been surpassed by recent approaches.
Abstract
Deep convolutional networks based super-resolution is a fast-growing field with numerous practical applications. In this exposition, we extensively compare 30+ state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark single image super-resolution. We introduce a taxonomy for deep-learning based super-resolution networks that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based and adversarial designs. We also provide comparisons between the models in terms of network complexity, memory footprint, model input and output, learning details, the type of network losses and important architectural differences (e.g., depth, skip-connections, filters). The extensive evaluation performed, shows the consistent and rapid growth…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Vision and Imaging · Image Processing Techniques and Applications
