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Recent progress in deep learning-based models has improved photo-realistic(or perceptual) single-image super-resolution significantly. However, despitetheir powerful performance, many methods are difficult to apply to real-worldapplications because of the heavy computational requirements. To facilitate theuse of a deep model under such demands, we focus on keeping the networkefficient while maintaining its performance. In detail, we design anarchitecture that implements a cascading mechanism on a residual network toboost the performance with limited resources via multi-level feature fusion. Inaddition, our proposed model adopts group convolution and recursive schemes inorder to achieve extreme efficiency. We further improve the perceptual qualityof the output by employing the adversarial learning paradigm and a multi-scalediscriminator approach. The performance of our method is investigated throughextensive internal experiments and benchmarks using various datasets. Ourresults show that our models outperform the recent methods with similarcomplexity, for both traditional pixel-based and perception-based tasks.

Efficient Deep Neural Network for Photo-realistic Image Super-Resolution