Blueprint Separable Residual Network for Efficient Image Super-Resolution (2205.05996v1)

Abstract: Recent advances in single image super-resolution (SISR) have achieved extraordinary performance, but the computational cost is too heavy to apply in edge devices. To alleviate this problem, many novel and effective solutions have been proposed. Convolutional neural network (CNN) with the attention mechanism has attracted increasing attention due to its efficiency and effectiveness. However, there is still redundancy in the convolution operation. In this paper, we propose Blueprint Separable Residual Network (BSRN) containing two efficient designs. One is the usage of blueprint separable convolution (BSConv), which takes place of the redundant convolution operation. The other is to enhance the model ability by introducing more effective attention modules. The experimental results show that BSRN achieves state-of-the-art performance among existing efficient SR methods. Moreover, a smaller variant of our model BSRN-S won the first place in model complexity track of NTIRE 2022 Efficient SR Challenge. The code is available at https://github.com/xiaom233/BSRN.

Overview of Blueprint Separable Residual Network for Efficient Image Super-Resolution

The field of Single Image Super-Resolution (SISR) has witnessed significant advancements, predominantly driven by the adoption of deep learning architectures. Despite their effectiveness, the computational burden of these models remains a barrier to their deployment on resource-constrained edge devices. The paper "Blueprint Separable Residual Network for Efficient Image Super-Resolution" proposes an innovative approach aimed at alleviating this issue by introducing the Blueprint Separable Residual Network (BSRN).

Core Contributions

The work presents two key innovations: the use of Blueprint Separable Convolution (BSConv) and the integration of sophisticated attention mechanisms to enhance the model's ability. BSConv is an improvement over traditional depth-wise separable convolutions, designed specifically to efficiently utilize intra-kernel correlations, leading to reduced computational redundancy. Combined with effective attention modules such as the Enhanced Spatial Attention (ESA) and the Contrast-Aware Channel Attention (CCA), the BSRN framework achieves superior performance in SISR tasks while maintaining efficiency.

Experimental Validation

The paper's claims are supported by extensive empirical evidence. BSRN outperformed existing efficiency-oriented super-resolution networks, establishing new benchmarks across several image quality metrics. Notably, a compact variant, BSRN-S, secured first place in the NTIRE 2022 Efficient Super-Resolution Challenge's model complexity track, underscoring the network's capacity to balance performance and efficiency.

Quantitatively, BSRN demonstrated marked improvements in PSNR and SSIM across multiple benchmark datasets like Set5, Set14, and Urban100, while maintaining a lower parameter and computation footprint compared to other models. These results are pivotal, showcasing BSRN's potential as a state-of-the-art method for efficient SISR.

Implications and Future Directions

The introduction of BSConv and its integration into the BSRN framework exemplifies a significant step towards minimizing the resource demands of super-resolution networks. This advancement holds promising implications for the deployment of high-performing SR models in edge devices and real-time applications, where processing capacity and power consumption are critical constraints.

In a broader context, this research highlights the importance of architectural innovations in enhancing model efficiency without compromising performance. Future developments could explore the adaptation of BSRN's strategies in other computer vision domains or investigate the combination of BSConv with emerging technologies like transformers, further augmenting the network's capabilities.

Conclusion

The BSRN paper contributes valuable insights and methodologies to the field of SISR. By creatively addressing the computational challenges associated with high-performing SR models, the proposed approach not only elevates the current standards but also paves the way for future research focused on the fusion of model efficiency and effectiveness. As the demand for real-time high-quality image processing on low-resource devices continues to grow, the work presented in this paper is of significant relevance and anticipation within both academic and industrial circles.