Efficient Image Transmission for Autonomous Systems Using Residual Dense Feature Networks Over LoRa Networks
DOI:
https://doi.org/10.31294/p.v27i1.7584Keywords:
Autonomous Systems, LoRa communication, Image transmission, Image processing, Super resolutionAbstract
Autonomous systems face challenges in transmitting high-quality images over bandwidth-constrained networks like LoRa, which operates at data rates of 0.3–50 kbps. This study proposes the Residual Dense Feature Network (RDF Net), a super-resolution model designed to optimize image transmission within the constraints of LoRa networks. By leveraging Contrast-Aware Channel Attention (CCA), Enhanced Spatial Attention (ESA), Blueprint Separable Convolution (BSConv), and a progressive approach, RDF Net achieves 20x upscaling, enabling low-resolution images (40x40 pixels) to be reconstructed into high-resolution outputs (800x800 pixels) on a central server. Experimental evaluations demonstrate that Model-4, combining CCA and ESA, delivers state-of-the-art perceptual quality and structural fidelity, while Model-3, using ESA, offers a computationally efficient alternative for resource-constrained scenarios. Simulations of LoRa’s bandwidth limitations reveal that transmitting a single 40x40 image requires approximately 0.208–0.56 seconds at a data rate of 50 kbps. While this demonstrates the feasibility of near real-time communication, the trade-off between latency and visual fidelity remains a critical consideration, particularly for latency-sensitive applications. These findings underscore RDF Net’s potential to address the challenges of high-quality visual communication in bandwidth-constrained environments, paving the way for enhanced autonomous system applications. Further optimization, including adaptive compression strategies, and testing on actual LoRa hardware are recommended to validate its performance in real-world scenarios and explore its applicability to diverse autonomous systems.
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Andonian, A., Park, T., Russell, B. C., Isola, P., Zhu, J.-Y., & Zhang, R. (2021). Contrastive Feature Loss for Image Prediction. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 19341943. https://api.semanticscholar.org/CorpusID:244117332
Baghel, N., Dubey, S. R., & Singh, S. K. (2023). PTSR: Patch Translator for Image Super-Resolution. ArXiv, abs/2310.13216. https://api.semanticscholar.org/CorpusID:264405985
Bai, H., & Liang, X. (2024). A very lightweight image super-resolution network. Scientific Reports, 14(1), 13850. https://doi.org/10.1038/s41598-024-64724-y
Chen, Y., Liu, S., & Wang, X. (2020). Learning Continuous Image Representation with Local Implicit Image Function. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 86248634. https://api.semanticscholar.org/CorpusID:229221619
Chudasama, V., & Upla, K. (2020). E-ProSRNet: An enhanced progressive single image super-resolution approach. Computer Vision and Image Understanding, 200, 103038. https://doi.org/10.1016/j.cviu.2020.103038
Chudasama, V., Upla, K., Raja, K., Ramachandra, R., & Busch, C. (2022). Compact and progressive network for enhanced single image super-resolutionComPrESRNet. The Visual Computer, 38(11), 36433665. https://doi.org/10.1007/s00371-021-02193-4
Dede, O. L., Jalajamony, H. M., & Fernandez, R. E. (2024). Image Transmission over LoRa-Based Networks: A Performance Study Using Image Compression and Reconstruction Methods. 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), 642643. https://doi.org/10.1109/CCNC51664.2024.10454687
Haase, D., & Amthor, M. (2020). Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1458814597. https://doi.org/10.1109/CVPR42600.2020.01461
Ju, R.-Y., Chen, C.-C., Chiang, J.-S., Lin, Y.-S., Chen, W.-H., & Chien, C.-T. (2023). Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy. http://arxiv.org/abs/2303.09190
Kong, F., Li, M., Liu, S., Liu, D., He, J., Bai, Y., Chen, F., & Fu, L. (2022). Residual Local Feature Network for Efficient Super-Resolution. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 765775. https://doi.org/10.1109/CVPRW56347.2022.00092
Kong, X., Zhao, H., Qiao, Y., & Dong, C. (2021). ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1201112020. https://doi.org/10.1109/CVPR46437.2021.01184
Li, G., & Zhu, Y. (2022). A Novel Dual Dense Connection Network for Video Super-resolution. CoRR, abs/2203.02723. https://doi.org/10.48550/ARXIV.2203.02723
Li, Z., Liu, Y., Chen, X., Cai, H., Gu, J., Qiao, Y., & Dong, C. (2022). Blueprint Separable Residual Network for Efficient Image Super-Resolution. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 832842. https://doi.org/10.1109/CVPRW56347.2022.00099
Li, Z., Wang, C., Wang, J., Ying, S., & Shi, J. (2021). Lightweight adaptive weighted network for single image super-resolution. Computer Vision and Image Understanding, 211, 103254. https://doi.org/https://doi.org/10.1016/j.cviu.2021.103254
Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., & Timofte, R. (2021). SwinIR: Image Restoration Using Swin Transformer. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 18331844. https://doi.org/10.1109/ICCVW54120.2021.00210
Liu, A., Li, S., & Chang, Y. (2022). Image super-resolution using progressive residual multi-dilated aggregation network. Signal, Image and Video Processing, 16(5), 12711279. https://doi.org/10.1007/s11760-021-02078-y
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 999210002. https://doi.org/10.1109/ICCV48922.2021.00986
Murray, D., Koziniec, T., & deSouza, A. (2021). Exploring the Characteristics and Limits of LoRaWAN. 2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), 110116. https://doi.org/10.1109/IoTaIS53735.2021.9628831
Park, S., & Lee, E. (2021). One-to-many Approach for Improving Super-Resolution. ArXiv, abs/2106.10437. https://api.semanticscholar.org/CorpusID:235490650
Pham, V. D., Vishnevsky, V., Nguyen, D. C., & Kirichek, R. (2023). LoRa Mesh Network for Image Transmission: An Experimental Study (Y. Koucheryavy & A. Aziz, Eds.; pp. 606617). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-30258-9_54
Qin, Y., Wang, J., Cao, S., Zhu, M., Sun, J., Hao, Z., & Jiang, X. (2024). SRBPSwin: Single-Image Super-Resolution for Remote Sensing Images Using a Global Residual Multi-Attention Hybrid Back-Projection Network Based on the Swin Transformer. Remote Sensing, 16(12). https://doi.org/10.3390/rs16122252
Shang, T., Dai, Q., Zhu, S., Yang, T., & Guo, Y. (2020). Perceptual Extreme Super Resolution Network with Receptive Field Block. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 17781787. https://doi.org/10.1109/CVPRW50498.2020.00228
Wang, Y., Liu, W., Sun, W., Meng, X., Yang, G., & Ren, K. (2023). A Progressive Feature Enhancement Deep Network for Large-Scale Remote Sensing Image Superresolution. IEEE Transactions on Geoscience and Remote Sensing, 61, 113. https://doi.org/10.1109/TGRS.2023.3310518
Zhang, G., Weng, H., Liu, R., Zhang, M., & Zhang, Z. (2022). Point Clouds Classification of Large Scenes based on Blueprint Separation Convolutional Neural Network. 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022, 525530. https://doi.org/10.1109/CSCWD54268.2022.9776241
Zhong Xueliang and Luo, J. (2023). Classification-Based and Lightweight Networks for Fast Image Super Resolution. In A. and A. P. and J. C. Iliadis Lazaros and Papaleonidas (Ed.), Artificial Neural Networks and Machine Learning ICANN 2023 (pp. 140152). Springer Nature Switzerland.
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