Efficient Deep Neural Network for Photo-realistic Image Super-Resolution
This work addresses the challenge of applying deep super-resolution models in real-world applications by improving efficiency, representing an incremental advancement in the field.
The authors tackled the problem of heavy computational requirements in deep learning-based photo-realistic image super-resolution by designing an efficient network architecture that maintains performance, achieving results that outperform recent methods with similar complexity in both pixel-based and perception-based tasks.
Recent progress in deep learning-based models has improved photo-realistic (or perceptual) single-image super-resolution significantly. However, despite their powerful performance, many methods are difficult to apply to real-world applications because of the heavy computational requirements. To facilitate the use of a deep model under such demands, we focus on keeping the network efficient while maintaining its performance. In detail, we design an architecture that implements a cascading mechanism on a residual network to boost the performance with limited resources via multi-level feature fusion. In addition, our proposed model adopts group convolution and recursive schemes in order to achieve extreme efficiency. We further improve the perceptual quality of the output by employing the adversarial learning paradigm and a multi-scale discriminator approach. The performance of our method is investigated through extensive internal experiments and benchmarks using various datasets. Our results show that our models outperform the recent methods with similar complexity, for both traditional pixel-based and perception-based tasks.