An Attention-Based Approach for Single Image Super Resolution

TL;DR

This paper introduces an attention-based method for single image super resolution that identifies high frequency areas to enhance textures, improving image clarity and detail over existing methods.

Contribution

The paper presents a novel attention mechanism for SISR that locates high frequency details and a new DenseRes block network architecture for better feature integration.

Findings

Significant improvement over state-of-the-art in benchmark tests

Effective high frequency detail enhancement

Enhanced visual quality of super-resolved images

Abstract

The main challenge of single image super resolution (SISR) is the recovery of high frequency details such as tiny textures. However, most of the state-of-the-art methods lack specific modules to identify high frequency areas, causing the output image to be blurred. We propose an attention-based approach to give a discrimination between texture areas and smooth areas. After the positions of high frequency details are located, high frequency compensation is carried out. This approach can incorporate with previously proposed SISR networks. By providing high frequency enhancement, better performance and visual effect are achieved. We also propose our own SISR network composed of DenseRes blocks. The block provides an effective way to combine the low level features and high level features. Extensive benchmark evaluation shows that our proposed method achieves significant improvement over the state-of-the-art works in SISR.