Single-frame super-resolution with deep residual network-generative adversarial networks

Abstract

Developing and evaluating a deep learning-based method to enhance satellite image resolution has emerged as a promising approach to address challenges posed by motion, imaging blur, and noise without modifying existing optical systems. This study utilized an enhanced super-resolution generative adversarial network (SRGAN) with ResNet-50 as the generator and a modified VGG-19 in the discriminator. The model was trained on remote sensing images from the Linear Imaging Self-Scanning imagery and compared with very deep super resolution, SRGAN, and enhanced SRGAN methods using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) as evaluation metrics. Utilizing an enhanced SRGAN with ResNet-50 and modified VGG-19 significantly improved satellite image resolution. The proposed method consistently outperformed conventional convolutional neural network- and generative adversarial network-based super-resolution techniques. Across three test datasets, the method achieved SSIM scores as high as 0.862 and PSNR scores of 33.256, 32.886, and 34.885, demonstrating its superior ability to preserve image properties and enhance resolution. The incorporation of perceptual loss alongside pixel loss contributed to improved visual quality, making the approach particularly effective in maintaining fine details and naturalistic high-frequency characteristics.

Keywords

Generative Adversarial Network / Linear Imaging Self-Scanning Image / Peak Signal-to-Noise Ratio / ResNet-50 / Structural Similarity Index Measure / Super Resolution

References

[1] Adarsh P., Rathi P., & Kumar M. (2020). YOLO v3-tiny: Object detection and recognition using one stage improved model. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India. IEEE, United States, p687-694. https://doi.org/10.1109/ICACCS48705.2020.9074315

[2] Bu L., Zhang J., Zhang Z., Yang Y., & Deng M. (2024). Deep learning for integrated speckle reduction and super-resolution in multi-temporal SAR. Remote Sensing, 16(1), 18. https://doi.org/10.3390/rs16010018

[3] Dileep R., Jayanth J., Choodarathnakar A.L., & Ravikiran H.K. (2025). Walrus optimization algorithm for panchromatic and multispectral image fusion. Remote Sensing Applications: Society and Environment, 38, 101562. https://doi.org/10.1016/j.rsase.2025.101562

[4] Dileep R., Jayanth J., Ravikiran H.K., Vedha A.S., Shyamala C., & Yuvaraju T. (2024). Enhancing image fusion via optimized BAT algorithm in Brovey transform for remote sensing images. In: 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). p1-6. https://doi.org/10.1109/ICKECS61492.2024.10617149

[5] Dong R., Zhang L., & Fu H. (2022). RRSGAN: Reference-based super-resolution for remote sensing image. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-17. https://doi.org/10.1109/TGRS.2020.3046045

[6] Frizza T., Dansereau D.G., Seresht N.M., & Bewley M. (2022). Semantically accurate super-resolution generative adversarial networks. Computer Vision and Image Understanding, 221, 103464. https://doi.org/10.1016/j.cviu.2022.103464

[7] Han R, Mei B., Huang X., Xue H., Jiang X., & Yang S. (2023). Remote sensing image super-resolution adversarial network based on reverse feature fusion and residual feature dilation. IEEE Access, 11, 85259-85267. https://doi.org/10.1109/ACCESS.2023.3304050

[8] Haut J.M., Fernandez-Beltran R., Paoletti M.E., Plaza J., Plaza A., & Pla F. (2018). A new deep generative network for unsupervised remote sensing single-image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 56(11), 6792-6810.

[9] He Z., Li J., Liu L., He D., & Xiao M. (2022). Multiframe video satellite image super-resolution via attention-based residual learning. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-17. https://doi.org/10.1109/TGRS.2021.3072381 https://doi.org/10.1109/TGRS.2018.2843525

[10] Jayanth J., Ravikiran H.K., Shyamala C., & Dileep R. (2025). Detecting the stages of ragi crop diseases using satellite data in villages of Nanjangud Taluk. In: SinghS., Sood, V., Srivastav, A.L., & Ampatzidis Y., editors. Hyperautomation in Precision Agriculture. Academic Press, United States, p131-145. https://doi.org/10.1016/B978-0-443-24139-0.00011-7

[11] Jiang K., Wang Z., Yi P., Jiang J., Xiao J., & Yao Y. (2018). Deep distillation recursive network for remote sensing imagery super-resolution. Remote Sensing, 10(11), 1700. https://doi.org/10.3390/rs10111700

[12] Khan B., Mumtaz A., Zafar Z., Sedkey M., Benkhelifa E., & Moazam M. (2023). CGA-Net: Channel-wise gated attention network for improved super-resolution in remote sensing imagery. Machine Vision and Applications, 34, 128. https://doi.org/10.1007/s00138-023-01477-0

[13] Lei S., Shi Z., & Zou Z. (2017). Super-resolution for remote sensing images via local-global combined network. IEEE Geoscience and Remote Sensing Letters, 14(8), 1243-1247. https://doi.org/10.1109/LGRS.2017.2705985

[14] Li J., Zheng K., Gao L., Han Z., Li Z., & Chanussot J. (2025). Enhanced deep image prior for unsupervised hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 63, 1-18. https://doi.org/10.1109/TGRS.2025.3531646

[15] Li J., Zheng K., Gao L., Ni L., Huang M., & Chanussot J. (2024). Model-informed multistage unsupervised network for hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-17. https://doi.org/10.1109/TGRS.2024.3391014

[16] Li J., Zheng K., Gao L., Ni L., Xiao M., & Chanussot J. (2023). X-shaped interactive autoencoders with cross-modality mutual learning for unsupervised hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-17. https://doi.org/10.1109/TGRS.2023.3300043

[17] Li J., Zheng K., Liu W., Li Z., Yu H., & Ni L. (2023). Model-guided coarse-to-fine fusion network for unsupervised hyperspectral image super-resolution. IEEE Geoscience and Remote Sensing Letters, 20, 1-5. https://doi.org/10.1109/LGRS.2023.3309854

[18] Li Q., Yang R., Xiao F., Bhanu B., & Zhang F. (2022). Attention-based anomaly detection in multi-view surveillance videos. Knowledge-Based Systems, 252, 109348. https://doi.org/10.1016/j.knosys.2022.109348

[19] Liu J., Yuan Z., Pan Z., Fu Y., Liu L., & Lu B. (2022). Diffusion model with detail complement for super-resolution of remote sensing. Remote Sensing, 14(19), 4834. https://doi.org/10.3390/rs14194834

[20] Min J., Lee Y., Kim D., & Yoo J. (2024). Bridging the domain gap: A simple domain matching method for reference-based image super-resolution in remote sensing. IEEE Geoscience and Remote Sensing Letters, 21, 1-5. https://doi.org/10.1109/LGRS.2024.3345678

[21] Pang B., Zhao S., & Liu Y. (2023). The use of a stable super-resolution generative adversarial network (SSRGAN) on remote sensing images. Remote Sensing, 15(20), 5064. https://doi.org/10.3390/rs15205064

[22] Ravikiran H.K., Jayanth J., Sathisha M.S., Yogeesh G.H., & Dileep R. (2024). Classification of crop across heterogeneous landscape through experienced artificial bee colony. SN Computer Science, 5, 428-435. https://doi.org/10.1007/s42979-024-02790-9

[23] Shi Y., Jiang C., Liu C., Li W., & Wu Z. (2023). A super-resolution reconstruction network of space target images based on dual regression and deformable convolutional attention mechanism. Electronics, 12(13), 2995. https://doi.org/10.3390/electronics12132995

[24] Sui J., Ma X., Zhang X., & Pun M.O. (2023). GCRDN: Global context-driven residual dense network for remote sensing image superresolution. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 4457-4468. https://doi.org/10.1109/JSTARS.2023.3273081

[25] Tang E., Wang L., Wang Y., Yu Y., & Zeng X. (2022). Lightweight frequency-based attention network for image super-resolution. Journal of Electronic Imaging, 31(5), 053014. https://doi.org/10.1117/1.JEI.31.5.053014

[26] Veganzones M.A., Simões M., Licciardi G., Yokoya N., Bioucas-Dias J.M., & Chanussot J. (2016). Hyperspectral super-resolution of locally low rank images from complementary multisource data. IEEE Transactions on Image Processing, 25(1), 274-288. https://doi.org/10.1109/TIP.2015.2496263

[27] Wang C., Zhang X., Yang W., Li X., Lu B., & Wang J. (2023). MSAGAN: A new super-resolution algorithm for multispectral remote sensing image based on a multiscale attention GAN network. IEEE Geoscience and Remote Sensing Letters, 20, 1-5. https://doi.org/10.1109/LGRS.2023.3258965

[28] Wang J., Gao K., Zhang Z., Ni C., Hu Z., Chen D., & Wu Q. (2021). Multisensor remote sensing imagery super-resolution with conditional GAN. Journal of Remote Sensing, 2021, 9829706. https://doi.org/10.34133/2021/9829706

[29] Wang P., Bayram B., & Sertel E. (2022). A comprehensive review on deep learning based remote sensing image super-resolution methods. Earth-Science Reviews, 232, 104110. https://doi.org/10.1016/j.earscirev.2022.104110

[30] Wang X., Sun L., Chehri A., & Song Y. (2023). A review of GAN-based super-resolution reconstruction for optical remote sensing images. Remote Sensing, 15(20), 5062. https://doi.org/10.3390/rs15205062

[31] Wang X., Yi J., Guo J., Song Y., Lyu J., Xu J., et al. (2022). A review of image super-resolution approaches based on deep learning and applications in remote sensing. Remote Sensing, 14, 5423. https://doi.org/10.3390/rs1421542

[32] Wang Y., Lyu B., Shi C., & Hu Y. (2023). Non-parametric simulation of random field samples from incomplete measurements using generative adversarial networks. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 18, 60-84. https://doi.org/10.1080/17499518.2023.2222383

[33] Wang Y., Shao Z., Lu T., Wang J., Cheng G., Zuo X., & Dang C. (2024). Remote sensing pan-sharpening via cross-spectral-spatial fusion network. IEEE Geoscience and Remote Sensing Letters, 21, 37844. https://doi.org/10.1109/LGRS.2023.3337844

[34] Zhang L., Dong R., Yuan S., Li W., Zheng J., & Fu H. (2021). Making low-resolution satellite images reborn: A deep learning approach for super-resolution building extraction. Remote Sensing, 13(15), 2872. https://doi.org/10.3390/rs13152872

[35] Zhang T., Chen H., Chen S., & Bian C. (2022). Edge-enhanced efficient network for remote sensing image super-resolution. International Journal of Remote Sensing, 43(14), 5324-5348. https://doi.org/10.1080/01431161.2022.2076217

[36] Zhang Z., Gao K., Wang J., Min L., Ji S., Ni C., & Chen D. (2022). Gradient enhanced dual regression network: Perception-preserving super-resolution for multi-sensor remote sensing imagery. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. https://doi.org/10.1109/LGRS.2021.3134798