Efficient Image Super-Resolution using Vast-Receptive-Field Attention
This work addresses the need for more efficient image super-resolution models, particularly for resource-constrained applications, though it is incremental as it builds on existing attention-based methods.
The paper tackled the problem of designing efficient super-resolution networks by improving attention mechanisms, resulting in VapSR, which outperforms lightweight networks with significantly fewer parameters, such as using only 21.68% and 28.18% of parameters compared to IMDB and RFDN for similar performance.
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.