Kernel-attended residual network for single image super-resolution

Single image super-resolution is very important as a low-level computer vision task. With the development of deep convolution neural networks (CNNs), recent approaches with CNNs have outperformed existing traditional methods in the single image super-resolution (SISR) field. However, these methods may suffer from weaker representational power and overly-smoothing textures. To handle these problems, we propose a Kernel-Attended Residual Network (KARN). Our KARN possesses the optimal performance for feature fusion and feature representation. Specifically, we present a multi-channel fusion block (MCFB) to restore plentiful textual feature information, and a kernel-attended block (KAB) to improve the representation power of our network with multiple kernels. Besides, we present a space-feature re-calibration block (SFRB) to integrate the calibration into features in the spatial aspect. Owing to the advanced information that we extract, KARN achieves a more notable performance than state-of-the-art methods by evaluating the performance of results based on benchmark datasets. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The paper introduces single image super-resolution as an important computer vision task and proposes a new Kernel-Attended Residual Network (KARN) to address the shortcomings of traditional methods. KARN excels in feature fusion, feature representation, and extracts more advanced information, achieving significantly better performance than existing methods.