Triple Attention Mixed Link Network for Single Image Super Resolution

TL;DR

This paper introduces the Triple Attention Mixed Link Network (TAN) for single image super resolution, which enhances feature representation through multi-kernel, spatial, and channel attention mechanisms combined with residual and dense connections, leading to improved image quality.

Contribution

The proposed TAN architecture integrates three attention mechanisms with mixed link connections, offering a novel approach to improve super resolution performance over existing methods.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Effectively captures multi-scale features with multi-kernel attention.

Enhances image details through spatial and channel attention mechanisms.

Abstract

Single image super resolution is of great importance as a low-level computer vision task. Recent approaches with deep convolutional neural networks have achieved im-pressive performance. However, existing architectures have limitations due to the less sophisticated structure along with less strong representational power. In this work, to significantly enhance the feature representation, we proposed Triple Attention mixed link Network (TAN) which consists of 1) three different aspects (i.e., kernel, spatial and channel) of attention mechanisms and 2) fu-sion of both powerful residual and dense connections (i.e., mixed link). Specifically, the network with multi kernel learns multi hierarchical representations under different receptive fields. The output features are recalibrated by the effective kernel and channel attentions and feed into next layer partly residual and partly dense, which filters the information and enable the network to learn more powerful representations. The features finally pass through the spatial attention in the reconstruction network which generates a fusion of local and global information, let the network restore more details and improves the quality of reconstructed images. Thanks to the diverse feature recalibrations and the advanced information flow topology, our proposed model is strong enough to per-form against the state-of-the-art methods on the bench-mark evaluations.