Efficient Image Super-Resolution using Vast-Receptive-Field Attention (2210.05960v1)

Abstract: The attention mechanism plays a pivotal role in designing advanced super-resolution (SR) networks. In this work, we design an efficient SR network by improving the attention mechanism. We start from a simple pixel attention module and gradually modify it to achieve better super-resolution performance with reduced parameters. The specific approaches include: (1) increasing the receptive field of the attention branch, (2) replacing large dense convolution kernels with depth-wise separable convolutions, and (3) introducing pixel normalization. These approaches paint a clear evolutionary roadmap for the design of attention mechanisms. Based on these observations, we propose VapSR, the VAst-receptive-field Pixel attention network. Experiments demonstrate the superior performance of VapSR. VapSR outperforms the present lightweight networks with even fewer parameters. And the light version of VapSR can use only 21.68% and 28.18% parameters of IMDB and RFDN to achieve similar performances to those networks. The code and models are available at https://github.com/zhoumumu/VapSR.

Efficient Image Super-Resolution Using Vast-Receptive-Field Attention

The paper presents VapSR, a novel architecture designed to advance the field of Single Image Super-Resolution (SISR) by optimizing the attention mechanisms within convolutional neural networks. Leveraging vast receptive fields, depth-wise separable convolutions, and pixel normalization, VapSR achieves improved performance with significantly fewer parameters than previous models.

Technical Overview

The efficiency of VapSR is rooted in three primary innovations. Firstly, the paper investigates the benefits of increasing the receptive field in attention branches, drawing parallels with existing trends in vision transformers and large kernel designs such as ConvNeXt and RepLKNet. These approaches utilize attention mechanisms that can process more spatial information, resulting in enhanced image reconstruction capabilities.

Secondly, VapSR employs depth-wise separable convolutions rather than dense large kernel convolutions, reducing the computational load while maintaining a large effective receptive field. This strategic design choice is comparable to techniques found in VAN and large kernel attention models, which prioritize sparse convolution operations to manage computational complexity.

Thirdly, the paper introduces pixel normalization as an effective tool to stabilize training. This normalization technique is crucial for mitigating internal covariate shifts that can cause training instabilities, especially in architectures reliant on element-wise attention multipliers.

Experimental Results

Empirically, VapSR surpasses a range of state-of-the-art lightweight super-resolution models, including recent champions of the NTIRE 2022 Efficient SR Challenge. The model achieves significant gains in PSNR and SSIM across commonly used benchmarks like Set5, Set14, B100, and Urban100. For instance, VapSR $\times4$ demonstrates an average PSNR improvement of 0.187 dB using only 21.68% and 28.18% of the parameters of RFDN and IMDN, respectively. The VapSR-S variant, which is an even lighter version, maintains competitive performance, confirming VapSR's efficacy and efficiency.

Implications and Future Directions

The development of VapSR paves the way for more efficient image processing in both research and industry, particularly where computing resources are limited. Its reduced parameter footprint without compromising performance suggests applications in mobile devices and real-time processing systems, potentially democratizing access to high-quality image enhancement.

The paper's insights into attention mechanisms and normalization pave the way for further exploration of pixel-level normalization across different convolutional architectures. Future research can focus on refining these techniques and expanding their application domains, including potential extensions into other vision tasks like semantic segmentation and object detection.

In summary, VapSR represents a significant advancement in efficient image super-resolution design, offering a blueprint for future AI models that require optimized architecture concerning computational efficiency and training stability.