An Attention-Based Approach for Single Image Super Resolution
This work addresses the challenge of recovering high frequency details in super resolution for image processing applications, representing an incremental advancement by incorporating attention mechanisms into existing networks.
The paper tackles the problem of single image super resolution by addressing the lack of modules to identify high frequency details, which often leads to blurred outputs, and proposes an attention-based approach for high frequency compensation, achieving significant improvement over state-of-the-art methods in benchmark evaluations.
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.