Dual Aggregation Transformer for Image Super-Resolution (2308.03364v2)

Abstract: Transformer has recently gained considerable popularity in low-level vision tasks, including image super-resolution (SR). These networks utilize self-attention along different dimensions, spatial or channel, and achieve impressive performance. This inspires us to combine the two dimensions in Transformer for a more powerful representation capability. Based on the above idea, we propose a novel Transformer model, Dual Aggregation Transformer (DAT), for image SR. Our DAT aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner. Specifically, we alternately apply spatial and channel self-attention in consecutive Transformer blocks. The alternate strategy enables DAT to capture the global context and realize inter-block feature aggregation. Furthermore, we propose the adaptive interaction module (AIM) and the spatial-gate feed-forward network (SGFN) to achieve intra-block feature aggregation. AIM complements two self-attention mechanisms from corresponding dimensions. Meanwhile, SGFN introduces additional non-linear spatial information in the feed-forward network. Extensive experiments show that our DAT surpasses current methods. Code and models are obtainable at https://github.com/zhengchen1999/DAT.

Overview of "Dual Aggregation Transformer for Image Super-Resolution"

The paper introduces a novel Transformer-based model, the Dual Aggregation Transformer (DAT), aiming to enhance image super-resolution (SR) by aggregating spatial and channel features effectively. The paper addresses the limitations of traditional convolutional approaches which often struggle with capturing global dependencies crucial for high-quality image reconstruction.

Methodology

The authors propose the Dual Aggregation Transformer (DAT), which is characterized by its ability to perform feature aggregation across spatial and channel dimensions via both inter-block and intra-block mechanisms.

1. Inter-Block Feature Aggregation:
   • DAT alternates between using spatial window self-attention (SW-SA) and channel-wise self-attention (CW-SA) across successive Transformer blocks. This strategy allows the model to capture comprehensive spatial and channel contexts, optimizing the representation capabilities needed for SR tasks.
2. Intra-Block Feature Aggregation:
   • An Adaptive Interaction Module (AIM) is introduced to enhance the fusion of features from self-attention and convolutional branches. AIM uses spatial and channel interaction mechanisms to adaptively combine global and local information.
   • The Spatial-Gate Feed-Forward Network (SGFN) is incorporated to integrate additional nonlinear spatial information and address channel redundancy, strengthening the traditional feed-forward network's ability to handle spatial features.

Overall, these dual aggregation strategies are designed to achieve superior feature representation, facilitating high-quality image reconstruction.

Experimental Results

The authors conduct extensive experiments across several benchmark datasets, using upscaling factors of ×2, ×3, and ×4. The results indicate that DAT consistently outperforms existing state-of-the-art methods, notably on challenging datasets such as Urban100 and Manga109.

• Performance Gains: The paper reports significant improvements in PSNR and SSIM metrics, showcasing DAT’s capability in generating sharper and more accurate images compared to other SR methods.
• Computation Efficiency: Compared with other models like SwinIR and CAT-A, DAT provides competitive performance with lower computation complexity (FLOPs) and parameter count.

Implications and Future Directions

The introduction of DAT marks an important step in leveraging Transformers for low-level vision tasks, specifically image super-resolution. The dual aggregation approach is proven to effectively integrate spatial and channel information, providing a robust framework for future research.

Potential developments may include:

• Further refinement of AIM and SGFN modules to optimize computational overhead.
• Exploration of DAT applications in other related vision tasks requiring enhanced detail preservation and reconstruction.
• Investigation into the integration of additional attention mechanisms to further augment the model’s adaptability and efficiency.

In conclusion, the Dual Aggregation Transformers present a significant advancement in image super-resolution through innovative feature aggregation strategies, standing as a promising foundation for future enhancements in both theoretical models and practical applications.