Lightweight Image Super-Resolution with Adaptive Weighted Learning Network (1904.02358v1)

Abstract: Deep learning has been successfully applied to the single-image super-resolution (SISR) task with great performance in recent years. However, most convolutional neural network based SR models require heavy computation, which limit their real-world applications. In this work, a lightweight SR network, named Adaptive Weighted Super-Resolution Network (AWSRN), is proposed for SISR to address this issue. A novel local fusion block (LFB) is designed in AWSRN for efficient residual learning, which consists of stacked adaptive weighted residual units (AWRU) and a local residual fusion unit (LRFU). Moreover, an adaptive weighted multi-scale (AWMS) module is proposed to make full use of features in reconstruction layer. AWMS consists of several different scale convolutions, and the redundancy scale branch can be removed according to the contribution of adaptive weights in AWMS for lightweight network. The experimental results on the commonly used datasets show that the proposed lightweight AWSRN achieves superior performance on x2, x3, x4, and x8 scale factors to state-of-the-art methods with similar parameters and computational overhead. Code is avaliable at: https://github.com/ChaofWang/AWSRN

Lightweight Image Super-Resolution with Adaptive Weighted Learning Network

The paper "Lightweight Image Super-Resolution with Adaptive Weighted Learning Network" introduces the Adaptive Weighted Super-Resolution Network (AWSRN) aimed at enhancing single-image super-resolution (SISR) performance while maintaining computational efficiency. The authors identify the prevalent challenge in convolutional neural network (CNN) based super-resolution models, which is the computational burden that restricts their applicability in real-world scenarios.

Methodology and Network Design

AWSRN forms the foundation of the proposed method, integrating novel components such as the Adaptive Weighted Residual Unit (AWRU) and the Adaptive Weighted Multi-Scale (AWMS) module. The local fusion block (LFB) employs a combination of AWRUs and a local residual fusion unit (LRFU) to facilitate robust residual learning. Notably, the AWMS module leverages multiple scale convolutions, allowing the network to efficiently utilize feature information during image reconstruction. This approach contrasts with traditional single-scale reconstruction layers that might underutilize available data, thereby enhancing the trade-off between computation and reconstruction quality.

Performance and Experimental Results

The empirical evaluation of AWSRN indicates superior performance across various scale factors (×2, ×3, ×4, and ×8) on standard benchmark datasets, namely Set5, Set14, B100, Urban100, and Manga109. The proposed network achieves a competitive advantage over state-of-the-art methods by balancing the quality of the reconstructed images and the computational resources required. Specifically, the AWSRN-S, AWSRN-SD, AWSRN-M, and AWSRN configurations exhibit remarkable PSNR and SSIM scores with lower parameter counts and Muti-Adds compared to existing models such as CARN and MSRN.

Technical Contributions and Implications

1. Adaptive Weighted Learning Mechanism: The introduction of AWRU and AWMS modules signifies a methodological advancement in adaptive feature learning, enabling the network to assign resource allocation dynamically based on feature importance. This adaptive mechanism circumvents the pitfalls of conventional residual scaling methods.
2. Efficient Residual Learning: LFB exemplifies a more integrated approach to residual learning, facilitating improved information flow across various network layers, which minimizes the risk of gradient explosion in deeper layers.
3. Practical Efficiency: The network's design prioritizes lightweight adaptability, making the AWSRN an appealing choice for applications requiring real-time processing efficiency without sacrificing the quality of super-resolution tasks.

Future Outlook

This paper hints at the potential of employing adaptive mechanisms and multi-scale fusion in broader contexts within computer vision. Future research might explore the integration of AWSRN with reinforcement learning paradigms for enhanced decision-making in resource-constrained environments. Additionally, the application of AWSRN principles to other domains such as video super-resolution could further delineate the capabilities and limitations of adaptive weighted learning networks.

Overall, AWSRN introduces a promising direction in the design of lightweight yet highly effective super-resolution networks, suggesting that incremental architectural innovations can substantively impact the efficiency and applicability of deep learning models in real-world settings.