Activating More Pixels in Image Super-Resolution Transformer (2205.04437v3)

Abstract: Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better reconstruction, we propose a novel Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement. Extensive experiments show the effectiveness of the proposed modules, and we further scale up the model to demonstrate that the performance of this task can be greatly improved. Our overall method significantly outperforms the state-of-the-art methods by more than 1dB. Codes and models are available at https://github.com/XPixelGroup/HAT.

• The paper introduces the Hybrid Attention Transformer (HAT) which combines channel attention and window-based self-attention to fully exploit pixel information.
• The overlapping cross-attention module enhances across-window connectivity, reducing blocking artifacts seen in traditional architectures like SwinIR.
• The same-task pre-training strategy leverages large-scale datasets, achieving over 1dB performance gains compared to state-of-the-art methods.

Overview of "Activating More Pixels in Image Super-Resolution Transformer"

The paper "Activating More Pixels in Image Super-Resolution Transformer" by Xiangyu Chen et al. investigates optimization strategies for transformer-based architectures in the image super-resolution (SR) domain. Traditional convolutional neural network (CNN) methods have dominated SR; however, transformers have recently shown promising results. The authors aim to address constraints in spatial information utilization within transform-based models.

Key Contributions

The paper introduces a novel architecture named the Hybrid Attention Transformer (HAT). This model aims to extend the operational capacity of input pixel utilization, leveraging the strengths of both channel attention and window-based self-attention.

1. Hybrid Attention Transformer (HAT): HAT amalgamates channel attention with window-based self-attention to utilize complementary strengths of these attention mechanisms. While channel attention can capitalize on global statistics, window-based self-attention focuses on local feature fitting.
2. Overlapping Cross-Attention Module: The authors propose an overlapping cross-attention module to improve across-window interactions within the transformer, addressing local connectivity issues apparent in existing architectures like SwinIR, which suffer from blocking artefacts due to limited window overlap.
3. Same-Task Pre-Training Strategy: A pre-training approach tailored to the same SR task is introduced. By employing large-scale datasets like ImageNet for task-specific model pre-training, a foundation is laid for fine-tuning on more focused datasets such as DF2K, yielding further performance enhancements.

Results and Implications

Extensive experiments demonstrate that these innovations substantially improve quantitative performance metrics, surpassing state-of-the-art methods by over 1dB in specific scenarios. For instance, the proposed HAT architecture substantially exceeds the performance of SwinIR by margins ranging from 0.3dB to 1.2dB, depending on the dataset and scaling factor.

The quantitative superiority of the HAT model underscores its enhanced capability in leveraging broader pixel ranges for reconstruction, leading to richer super-resolution outputs. The theoretical implication is a more effective exploitation of transformer models' potential in low-level vision tasks, traditionally dominated by CNN frameworks.

Future Directions

While the proposed HAT model has demonstrated notable advancements, the research opens avenues for further exploration in:

• Model Scalability: Further scaling the model while maintaining efficiency could uncover even more significant performance capabilities, expanding the practical application of these findings in deployments requiring real-time processing.
• Cross-Domain Adaptability: Extending these concepts into other low-level vision tasks could validate the robustness and generalizability of the overlapping attention mechanisms introduced.
• Pre-Training Paradigms: The impact of innovative pre-training strategies on other transform-based architectures could be examined, potentially redefining pre-training practices across vision tasks.

In conclusion, this paper makes substantive strides in refining opportunity areas within transformer-based SR methodologies, emphasizing the importance of both architectural enhancement and pre-training strategies in maximizing model potential. By introducing HAT, the authors not only advance current understanding but also lay a foundation for future scholarly inquiry in computer vision.