Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network (1803.08664v5)

Abstract: In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to real-world applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep network for image super-resolution. In detail, we design an architecture that implements a cascading mechanism upon a residual network. We also present variant models of the proposed cascading residual network to further improve efficiency. Our extensive experiments show that even with much fewer parameters and operations, our models achieve performance comparable to that of state-of-the-art methods.

• The paper introduces a cascading residual network that combines local and global feature aggregation for enhanced single-image super-resolution.
• It presents CARN and CARN-M, which deliver competitive PSNR and SSIM scores on benchmarks while significantly reducing computational load.
• This efficient design enables deployment on resource-constrained devices, promising real-time applications in areas such as mobile imaging and video streaming.

Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network

The paper "Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network" introduces a novel approach to performing Single-Image Super-Resolution (SISR) tasks by using a deep cascading residual network architecture. The primary objective of this research is to address the substantial computational burden commonly associated with deep learning-based SISR methods, without sacrificing accuracy.

Introduction

SISR is a challenging computer vision task aimed at reconstructing a high-resolution (HR) image from a low-resolution (LR) input. Traditional deep learning approaches for SISR, such as convolutional neural networks (CNNs), have demonstrated substantial improvements in image quality but also require significant computational resources, rendering them impractical for real-world applications with limited processing power, such as mobile devices.

Proposed Method

Cascading Residual Network (CARN)

The paper proposes the Cascading Residual Network (CARN), which introduces a cascading mechanism into a residual network structure. This is executed by embedding cascading blocks at both the local and global levels of the network, enhancing the flow of information through multiple layers and enabling multi-level representation learning. The local cascading aggregates features within a block, while the global cascading combines features across different blocks, culminating in a compact, yet highly effective form of feature extraction and reconstruction.

The CARN architecture is grounded on ResNet, but goes beyond by incorporating cascades of residual blocks interspersed with 1x1 convolution layers, thereby facilitating dynamic multi-level feature integration and expediting gradient propagation.

Efficient Cascading Residual Network (CARN-M)

To further enhance computational efficiency, the authors also proposed CARN-Mobile (CARN-M), which incorporates efficient residual blocks that utilize group convolutions. This variant aims to optimize the trade-off between computational load and performance. By replacing standard convolutions with grouped convolutions and employing a recursive network structure, CARN-M significantly reduces the number of operations and parameters, making it more suitable for deployment on resource-constrained devices.

Experimental Evaluation

The effectiveness of CARN and CARN-M was demonstrated through extensive experiments on several benchmark datasets, including Set5, Set14, B100, and Urban100. Both models showed competitive or superior results in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) compared to existing state-of-the-art methods, despite employing significantly fewer parameters and computational operations.

Numerical Results

CARN achieved compelling quantitative results:

• Set5 (x2): 37.76 PSNR / 0.9590 SSIM
• Set14 (x2): 33.52 PSNR / 0.9166 SSIM
• Urban100 (x2): 31.92 PSNR / 0.9256 SSIM

CARN-M, while being much leaner, also demonstrated strong performance:

• Set5 (x2): 37.53 PSNR / 0.9583 SSIM
• Set14 (x2): 33.26 PSNR / 0.9141 SSIM
• Urban100 (x2): 31.23 PSNR / 0.9193 SSIM

Furthermore, CARN-M required only 91.2G Multi-Adds and 412K parameters, underscoring its efficiency without compromising on quality.

Implications and Future Directions

The implications of this research are substantial for practical applications of SISR, particularly in contexts where computational resources are limited. The development of lightweight models like CARN-M opens up possibilities for real-time SISR on mobile devices, enhancing user experience in video streaming and surveillance systems.

Moreover, this work suggests potential extensions into other areas of computer vision that require efficient and high-performance image processing. Future research could explore the application of the cascading residual mechanism in video super-resolution and other related tasks, potentially leading to substantial storage and computational savings in media applications.

Conclusion

This paper presents a significant advancement in SISR by introducing the Cascading Residual Network (CARN) and its efficient variant CARN-M. These models achieve a balance between high accuracy and computational efficiency, making them highly suitable for real-world applications. The innovative cascading architecture offers robust multi-level feature representation while maintaining a lightweight profile, thus promising to reshape the landscape of effective, deployable SISR solutions.