Computer Vision
Reparameterizable Large Kernel Attention Networks for Infrared Image Super-Resolution
This paper introduces REPLKASR, a novel Reparameterizable Large Kernel Attention Network for infrared image super-resolution. It balances reconstruction performance and inference speed by using a multi-branch large kernel network during training that transforms into a single-branch network for inference. The method achieves state-of-the-art PSNR on infrared datasets and performs 4x super-resolution on 320x180 images in 37ms on an RK3588 NPU, addressing challenges in resource-constrained environments.
Unlocking High-Performance Infrared Imaging for Critical Operations
REPLKASR significantly enhances infrared image clarity and detail, which is crucial for applications like surveillance, autonomous navigation, and industrial inspection. By delivering superior image quality at real-time speeds on edge devices, it reduces hardware costs and operational latency, enabling more reliable and effective decision-making in demanding environments.
0.0008 dB
PSNR Improvement (dB)
37 ms
Inference Speed (ms)
x4
Scaling Factor
RK3588
Supported NPU
Deep Analysis & Enterprise Applications
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REPLKASR is composed of a shallow feature extraction module, a deep feature extraction module (cascaded ECB and REPLKA), and a high-quality image reconstruction module. During training, it uses a multi-branch large kernel network for comprehensive feature extraction, which is then reparameterized into a single-branch network for efficient inference. This allows for a balance between performance and speed, particularly crucial for resource-constrained edge devices. The REPLKA module, a core innovation, extends existing reparameterization techniques to larger 5x5 convolutional kernels, significantly expanding the receptive field.
The Large Kernel Reparameterization Attention mechanism (REPLKA) is a key innovation. It employs multiple 5x5 large convolutional kernels in parallel during training to increase the receptive field and enhance expressive capacity without increasing inference cost. During inference, these multiple branches are merged into a single-branch large kernel convolution, maintaining deployment efficiency. This approach addresses the limitations of smaller kernel optimizations by fundamentally expanding the receptive field while preserving computational efficiency.
To overcome the scarcity of large-scale infrared datasets, REPLKASR adopts a transfer learning strategy. The model is initially pretrained on larger visible light datasets (Flickr2K, DIV2K) to learn basic feature representations. Subsequently, it is fine-tuned on smaller infrared datasets (M3FD-100, Iray datasets) to achieve cross-modal knowledge transfer, enhancing robustness and reconstruction performance in infrared domains with limited data.
0.0008 dB
Average PSNR improvement on infrared datasets compared to state-of-the-art methods.
REPLKASR Inference Workflow
Input Low-Resolution Image
→
Shallow Feature Extraction (ECB)
→
Deep Feature Extraction (ECBs + REPLKA)
→
Residual Fusion
→
Upsampling (PixelShuffle)
→
Output High-Resolution Image
| Feature | Traditional Lightweight SR (e.g., ESPCN) | REPLKASR (Proposed) |
|---|---|---|
| Kernel Size | Typically 3x3 | 5x5 (Reparameterized) |
| Receptive Field | Limited | Expanded (via large kernel) |
| Inference Speed (320x180, 4x SR) | 44.24ms (ESPCN), 53.32ms (FSRCNN) | 37.59ms |
| PSNR (Average) | Lower | Higher (e.g., +0.0008dB) |
| Data Scarcity Handling | Relies on large datasets | Transfer learning (pre-trained on visible, fine-tuned on IR) |
Real-time Infrared Object Detection Enhancement
Problem: Infrared images often suffer from low resolution and blurriness, hindering the accuracy and confidence of object detection, especially for critical real-time applications like autonomous driving or surveillance.
Solution: The REPLKASR method was deployed on the RK3588 Neural Processing Unit for 4x super-resolution of 320x180 infrared images. Its large kernel reparameterization allowed for superior detail restoration and edge preservation.
Outcome: The super-resolved infrared images, when fed into YOLOv5 for object detection, consistently achieved higher confidence scores and fewer false negatives compared to other methods. For example, on Iray-boat and Iray-traffic datasets, REPLKASR-uint8 showed significantly higher detection confidence (e.g., 'boot 0.85' vs 'boot 0.72' for ESPCN). This demonstrates enhanced robustness and reliability in critical object detection tasks due to improved image quality.
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Annual Savings Potential
$0
Employee Hours Reclaimed Annually
0
Your Path to Advanced AI Implementation
A structured approach ensures seamless integration and maximum impact. Here’s a typical roadmap for bringing REPLKASR into your operations.
Phase 1: Initial Assessment & Data Preparation (2-4 Weeks)
Evaluate existing infrared data sources, define specific SR targets, and prepare datasets for model training and fine-tuning, including transfer learning strategies.
Phase 2: Model Customization & Training (4-8 Weeks)
Tailor the REPLKASR architecture to specific application needs and initiate training on visible and infrared datasets, leveraging transfer learning for optimal performance with limited infrared data.
Phase 3: Edge Device Deployment & Optimization (3-6 Weeks)
Deploy the reparameterized REPLKASR model onto target edge devices (e.g., RK3588 NPU), performing quantization and optimization for real-time inference speed and efficiency.
Phase 4: Validation & Integration (2-4 Weeks)
Conduct rigorous validation of super-resolution quality and inference speed in real-world scenarios, followed by integration into existing systems or new applications like object detection.
Schedule a complimentary 30-minute strategy session to explore how REPLKASR can enhance your enterprise's infrared imaging capabilities.
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