A Two-Stage Attentive Network for Single Image Super-Resolution

TL;DR

This paper introduces a two-stage attentive neural network that enhances single image super-resolution by effectively capturing contextual information and refining high-resolution details, leading to improved performance.

Contribution

The paper proposes a novel two-stage attentive network with multi-context and refined attention blocks for better feature extraction and image reconstruction in SISR.

Findings

Outperforms existing methods on four benchmark datasets.

Achieves higher quantitative metrics and better visual quality.

Demonstrates effectiveness of the two-stage attention mechanism.

Abstract

Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and contribute remarkable progress. However, most of the existing CNNs-based SISR methods do not adequately explore contextual information in the feature extraction stage and pay little attention to the final high-resolution (HR) image reconstruction step, hence hindering the desired SR performance. To address the above two issues, in this paper, we propose a two-stage attentive network (TSAN) for accurate SISR in a coarse-to-fine manner. Specifically, we design a novel multi-context attentive block (MCAB) to make the network focus on more informative contextual features. Moreover, we present an essential refined attention block (RAB) which could explore useful cues in HR space for reconstructing fine-detailed HR image. Extensive evaluations on four benchmark datasets demonstrate the efficacy of our proposed TSAN in terms of quantitative metrics and visual effects. Code is available at https://github.com/Jee-King/TSAN.