ShuffleMixer: An Efficient ConvNet for Image Super-Resolution (2205.15175v1)

Abstract: Lightweight and efficiency are critical drivers for the practical application of image super-resolution (SR) algorithms. We propose a simple and effective approach, ShuffleMixer, for lightweight image super-resolution that explores large convolution and channel split-shuffle operation. In contrast to previous SR models that simply stack multiple small kernel convolutions or complex operators to learn representations, we explore a large kernel ConvNet for mobile-friendly SR design. Specifically, we develop a large depth-wise convolution and two projection layers based on channel splitting and shuffling as the basic component to mix features efficiently. Since the contexts of natural images are strongly locally correlated, using large depth-wise convolutions only is insufficient to reconstruct fine details. To overcome this problem while maintaining the efficiency of the proposed module, we introduce Fused-MBConvs into the proposed network to model the local connectivity of different features. Experimental results demonstrate that the proposed ShuffleMixer is about 6x smaller than the state-of-the-art methods in terms of model parameters and FLOPs while achieving competitive performance. In NTIRE 2022, our primary method won the model complexity track of the Efficient Super-Resolution Challenge [23]. The code is available at https://github.com/sunny2109/MobileSR-NTIRE2022.

• The paper introduces a novel lightweight ConvNet architecture that combines large kernel depth-wise convolutions with channel split-shuffle operations to reduce computational cost.
• The proposed model reduces parameters and FLOPs by approximately sixfold while maintaining competitive super-resolution performance.
• The integration of Fused-MBConv enhances local detail reconstruction, balancing global feature interaction with efficient design for mobile deployment.

An In-depth Analysis of ShuffleMixer for Image Super-Resolution

Image super-resolution (SR) has long been a subject of considerable research interest, largely due to increasing demands from high-definition display devices. Recent advancements have relied heavily on convolutional neural networks (CNNs) to achieve noteworthy reconstruction performance, albeit at the expense of heavy computational requirements, which pose challenges for deployment in resource-constrained environments such as mobile devices. The paper "ShuffleMixer: An Efficient ConvNet for Image Super-Resolution" proposes an innovative approach that addresses these challenges effectively by introducing a lightweight model design, ShuffleMixer.

ShuffleMixer leverages a unique architecture combining large kernel depth-wise convolutions and channel split-shuffle operations to create a mobile-friendly SR solution. A notable feature is its use of large kernel ConvNet, a deviation from previous models that often stack multiple small kernel convolutions. This choice facilitates broader feature interaction, crucial for dense super-resolution tasks. Large depth-wise convolutions capture extensive spatial information, enhancing non-local feature modeling without the cost-heavy traditional convolutions.

Furthermore, the paper introduces channel splitting and shuffling strategies, borrowing elements from ShuffleNetV2, as a mechanism to significantly reduce computational costs while maintaining efficient channel mixing. The channel splitting divides the input tensor, allowing parallel processing before shuffling realigns these processed channels, thus promoting a comprehensive exchange of visual information across layers.

To mitigate the limitations posed by large depth-wise convolutions in modeling fine local details, the authors integrate Fused-MBConv—effectively boosting local connectivity and learning capacity within the network. This addition is instrumental in maintaining the model's efficiency without sacrificing performance quality. The experimental results demonstrate ShuffleMixer's capability to reduce model size approximately sixfold compared to existing state-of-the-art methods in parameters and FLOPs, while delivering highly competitive SR outcomes.

The implications of this work are multifaceted. Practically, it suggests model designs that can enable effective SR applications on mobile devices, expanding the potential user base for enhanced image processing tools without requiring substantial computational overhead. Theoretically, it contributes to the growing body of research advocating the utility of large kernel convolutions in CNN architectures, potentially influencing future designs for lightweight models in other vision tasks.

Looking forward, ShuffleMixer represents a promising direction for efficiency-driven AI model design. Potential future improvements could optimize kernel size further or refine the shuffling mechanisms to stabilize performance gains across various image datasets. Moreover, the work aligns well with emerging trends in vision tasks, underlining the importance of balancing computational efficiency with performance reliability in constrained environments.

In conclusion, the ShuffleMixer paper provides a compelling case for large-kernel ConvNets in image super-resolution, effectively addressing the balance between complexity, latency, and quality. It opens avenues for deploying advanced SR technology on mobile platforms—an essential step toward democratizing access to high-quality image processing tools.