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Derin Öğrenme ve Seyrek Temsil Tabanlı Hibrit Bir Yöntem ile Hiperspektral Görüntü Süper Çözünürlüğü

Hyperspectral Image Super-Resolution Using a Hybrid Method Combining Deep Learning and Sparse Representation

Hüseyin Aydilek(KIRIKKALE ÜNİVERSİTESİ, MÜHENDİSLİK VE MİMARLIK FAKÜLTESİ)0000-0003-3051-4259
Nihat İnanç(HALİÇ ÜNİVERSİTESİ)0000-0003-2989-6632
Sayfa
—
Yayın Tarihi
2026-10-05
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bulgular, derin öğrenme tabanlı uzamsal iyileştirme ile seyrek temsil tabanlı spektral füzyonun birlikte kullanılmasının, değerlendirilen koşullarda uzamsal ve spektral hata değerlerinin azaltılmasına katkı sağlayabileceğini göstermektedir.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Hiperspektral görüntüler, çok sayıda spektral bant içermeleri sayesinde nesnelere ait ayrıntılı bilgi sağlar; ancak görüntüleme sistemlerinin donanımsal kısıtları ve uzun görüntüleme mesafesi nedeniyle uzamsal çözünürlükleri çoğunlukla düşük kalmaktadır.
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Temel Bulgular & Veri:Derin evrişimsel ağ ve derin kalıntı ağ olmak üzere iki model, CAVE ve Harvard veri kümelerinde ×2, ×3 ve ×4 ölçek faktörlerinde RMSE ve SAM ölçütleri kullanılarak değerlendirilmiştir.
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Sonuç & Katkı:Bulgular, derin öğrenme tabanlı uzamsal iyileştirme ile seyrek temsil tabanlı spektral füzyonun birlikte kullanılmasının, değerlendirilen koşullarda uzamsal ve spektral hata değerlerinin azaltılmasına katkı sağlayabileceğini göstermektedir.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Hiperspektral görüntü · Süper çözünürlük · Derin öğrenme

Özet

Hiperspektral görüntüler, çok sayıda spektral bant içermeleri sayesinde nesnelere ait ayrıntılı bilgi sağlar; ancak görüntüleme sistemlerinin donanımsal kısıtları ve uzun görüntüleme mesafesi nedeniyle uzamsal çözünürlükleri çoğunlukla düşük kalmaktadır. Bu çalışmada, düşük uzamsal çözünürlüklü hiperspektral görüntülerin çözünürlüğünü artırmak amacıyla derin öğrenme ile seyrek temsili birleştiren hibrit bir süper çözünürlük yöntemi incelenmiştir. Önerilen yapıda uzamsal çözünürlük derin öğrenme modelleriyle iyileştirilirken, özgün düşük çözünürlüklü hiperspektral küpteki spektral bilginin yeniden oluşturulan görüntüye aktarılması Bayesçi seyrek temsil tabanlı sözlük öğrenme ile desteklenmiştir. Hesaplama yükünü azaltmak amacıyla iki boyutlu evrişim, her üç banttan seçilen bir banda uygulanmıştır. Derin evrişimsel ağ ve derin kalıntı ağ olmak üzere iki model, CAVE ve Harvard veri kümelerinde ×2, ×3 ve ×4 ölçek faktörlerinde RMSE ve SAM ölçütleri kullanılarak değerlendirilmiştir. Veri kümesi ortalamalarına göre derin kalıntı ağ tabanlı hibrit model, değerlendirilen yöntemler arasında tüm ölçek faktörlerinde en düşük RMSE ve SAM değerlerini vermiştir. CAVE veri kümesinde RMSE/SAM değerleri ×2, ×3 ve ×4 için sırasıyla 2,0146/2,8507, 2,1380/4,0742 ve 2,4712/5,1211; Harvard veri kümesinde ise 1,5753/2,1328, 1,7373/2,7135 ve 2,1524/3,3576 olarak elde edilmiştir. SCT_SDCNN ile karşılaştırıldığında SAM değerlerindeki azalma CAVE için %19,12-%24,80, Harvard için %21,93-%26,02 aralığındadır. Bulgular, derin öğrenme tabanlı uzamsal iyileştirme ile seyrek temsil tabanlı spektral füzyonun birlikte kullanılmasının, değerlendirilen koşullarda uzamsal ve spektral hata değerlerinin azaltılmasına katkı sağlayabileceğini göstermektedir. Yöntemin hedef veriye benzer özelliklerde eğitim verisine ihtiyaç duyması temel sınırlılıklardan biridir.

Abstract

Background/Aim: Hyperspectral images provide rich spectral information but often suffer from low spatial resolution due to hardware limitations and long imaging distances. This study aims to enhance the spatial resolution of low-resolution hyperspectral images while preserving their spectral information by proposing a hybrid super-resolution method that combines deep learning and sparse representation. Methods: The proposed framework comprises two components. Spatial resolution is enhanced using deep learning models, while the spectral information of the original low-resolution hyperspectral cube is reintroduced into the reconstructed image through Bayesian sparse-representation-based dictionary learning. A deep convolutional network and a deep residual network were adapted to hyperspectral images. To reduce the convolutional workload, two-dimensional convolution was applied to one band selected from every three bands. The method was evaluated on the CAVE and Harvard datasets at scale factors of ×2, ×3, and ×4 using root mean square error (RMSE) and spectral angle mapper (SAM), and was compared with bicubic interpolation, GFPCA, and SCT_SDCNN. Results: At the dataset-average level, the deep residual network-based hybrid model yielded the lowest RMSE and SAM values among the evaluated methods at all three scale factors. On CAVE, the RMSE/SAM values were 2.0146/2.8507, 2.1380/4.0742, and 2.4712/5.1211 at ×2, ×3, and ×4, respectively. The corresponding values on Harvard were 1.5753/2.1328, 1.7373/2.7135, and 2.1524/3.3576. Relative to SCT_SDCNN, the SAM reductions ranged from 19.12% to 24.80% on CAVE and from 21.93% to 26.02% on Harvard. Conclusion: The findings suggest that combining deep-learning-based spatial enhancement with sparse-representation-based spectral fusion can support lower spatial and spectral reconstruction errors under the evaluated conditions. The need for training data with characteristics comparable to the target data remains a limitation.

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Bu Makaleye Atıf Yap

Aydilek, H., İnanç, N. (2026). Derin Öğrenme ve Seyrek Temsil Tabanlı Hibrit Bir Yöntem ile Hiperspektral Görüntü Süper Çözünürlüğü. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, Cilt: 42 Sayı: 3. https://doi.org/10.65520/erciyesfen.1971600

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Kaynakça (80)

  1. Goel, M., Whitmire, E., Mariakakis, A., Saponas, T. S., Joshi, N., Morris, D., Guenter, B., Gavriliu, M., Borriello, G., & Patel, S. N. (2015). HyperCam: Hyperspectral imaging for ubiquitous computing applications. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’15) (pp. 145–156). Osaka, Japan: ACM. https://doi.org/10.1145/2750858.2804282
  2. Jensen, J. R. (2005). Introductory digital image processing: A remote sensing perspective (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-145361-6.
  3. Shaw, G. A., & Burke, H. K. (2003). Spectral imaging for remote sensing. Lincoln Laboratory Journal, 14(1), 3–28.
  4. Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., & Sousa, J. J. (2017). Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sensing, 9(11), 1110. https://doi.org/10.3390/rs9111110
  5. Briottet, X., Boucher, Y., Dimmeler, A., Malaplate, A., Cini, A., Diani, M., Bekman, H., Schwering, P., Skauli, T., Kasen, I., Renhorn, I., Klasén, L., Gilmore, M., & Oxford, D. (2006). Military applications of hyperspectral imagery. In Targets and Backgrounds XII: Characterization and Representation (Proc. SPIE, Vol. 6239, Art. 62390B, pp. 82–89). Bellingham, WA: SPIE. https://doi.org/10.1117/12.672030
  6. Lu, G., & Fei, B. (2014). Medical hyperspectral imaging: A review. Journal of Biomedical Optics, 19(1), 010901. https://doi.org/10.1117/1.JBO.19.1.010901
  7. Keshava, N., & Mustard, J. F. (2002). Spectral unmixing. IEEE Signal Processing Magazine, 19(1), 44–57. https://doi.org/10.1109/79.974727
  8. Eismann, M. T., & Hardie, R. C. (2004). Application of the stochastic mixing model to hyperspectral resolution enhancement. IEEE Transactions on Geoscience and Remote Sensing, 42(9), 1924–1933. https://doi.org/10.1109/TGRS.2004.830644
  9. Hardie, R. C., Eismann, M. T., & Wilson, G. L. (2004). MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor. IEEE Transactions on Image Processing, 13(9), 1174–1184. https://doi.org/10.1109/TIP.2004.829779
  10. Zhukov, B., Oertel, D., Lanzl, F., & Reinhackel, G. (1999). Unmixing-based multisensor multiresolution image fusion. IEEE Transactions on Geoscience and Remote Sensing, 37(3), 1212–1226. https://doi.org/10.1109/36.763276
  11. Wei, Q., Dobigeon, N., & Tourneret, J.-Y. (2014). Bayesian fusion of multispectral and hyperspectral images with unknown sensor spectral response. In 2014 IEEE International Conference on Image Processing (ICIP) (pp. 698–702). Paris, France. https://doi.org/10.1109/ICIP.2014.7025140
  12. Zhang, Y., De Backer, S., & Scheunders, P. (2009). Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 47(11), 3834–3843. https://doi.org/10.1109/TGRS.2009.2017737
  13. Palsson, F., Sveinsson, J. R., Ulfarsson, M. O., & Benediktsson, J. A. (2015). Model-based fusion of multi- and hyperspectral images using PCA and wavelets. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2652–2663. https://doi.org/10.1109/TGRS.2014.2363477
  14. Bendoumi, M. A., He, M., & Mei, S. (2014). Hyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 52(10), 6574–6583. https://doi.org/10.1109/TGRS.2014.2298056
  15. Yokoya, N., Yairi, T., & Iwasaki, A. (2012). Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion. IEEE Transactions on Geoscience and Remote Sensing, 50(2), 528–537. https://doi.org/10.1109/TGRS.2011.2161320
  16. Li, S., Dian, R., Fang, L., & Bioucas-Dias, J. M. (2018). Fusing hyperspectral and multispectral images via coupled sparse tensor factorization. IEEE Transactions on Image Processing, 27(8), 4118–4130. https://doi.org/10.1109/TIP.2018.2836307
  17. Zhang, K., Wang, M., Yang, S., & Jiao, L. (2018). Spatial–spectral-graph-regularized low-rank tensor decomposition for multispectral and hyperspectral image fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(4), 1030–1040. https://doi.org/10.1109/JSTARS.2017.2785411
  18. Akhtar, N., Shafait, F., & Mian, A. (2015). Bayesian sparse representation for hyperspectral image super resolution. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3631–3640). Boston, MA, USA. https://doi.org/10.1109/CVPR.2015.7298986
  19. Huang, B., Song, H., Cui, H., Peng, J., & Xu, Z. (2014). Spatial and spectral image fusion using sparse matrix factorization. IEEE Transactions on Geoscience and Remote Sensing, 52(3), 1693–1704. https://doi.org/10.1109/TGRS.2013.2253612
  20. Wei, Q., Bioucas-Dias, J. M., Dobigeon, N., & Tourneret, J.-Y. (2014). Fusion of multispectral and hyperspectral images based on sparse representation. In 2014 22nd European Signal Processing Conference (EUSIPCO) (pp. 1577–1581). Lisbon, Portugal.
  21. Han, X.-H., & Chen, Y.-W. (2019). Deep residual network of spectral and spatial fusion for hyperspectral image super-resolution. In 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM) (pp. 266–270). Singapore. https://doi.org/10.1109/BigMM.2019.00-13
  22. Han, X.-H., Shi, B., & Zheng, Y. (2018). SSF-CNN: Spatial and spectral fusion with CNN for hyperspectral image super-resolution. In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 2506–2510). Athens, Greece. https://doi.org/10.1109/ICIP.2018.8451142
  23. Palsson, F., Sveinsson, J. R., & Ulfarsson, M. O. (2017). Multispectral and hyperspectral image fusion using a 3-D-convolutional neural network. IEEE Geoscience and Remote Sensing Letters, 14(5), 639–643. https://doi.org/10.1109/LGRS.2017.2668299
  24. Zhou, F., Hang, R., Liu, Q., & Yuan, X. (2019). Pyramid fully convolutional network for hyperspectral and multispectral image fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(5), 1549–1558. https://doi.org/10.1109/JSTARS.2019.2910990
  25. Liao, W., Huang, X., Van Coillie, F., Gautama, S., Pizurica, A., Philips, W., Liu, H., Zhu, T., Shimoni, M., Moser, G., & Tuia, D. (2015). Processing of multiresolution thermal hyperspectral and digital color data: Outcome of the 2014 IEEE GRSS Data Fusion Contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 2984–2996. https://doi.org/10.1109/JSTARS.2015.2420582
  26. Simsek, M., & Polat, E. (2021). Performance evaluation of pan-sharpening and dictionary learning methods for sparse representation of hyperspectral super-resolution. Signal, Image and Video Processing, 15, 1099–1106. https://doi.org/10.1007/s11760-020-01836-8
  27. Hu, J.-F., Huang, T.-Z., Deng, L.-J., Dou, H.-X., Hong, D., & Vivone, G. (2022). Fusformer: A transformer-based fusion network for hyperspectral image super-resolution. IEEE Geoscience and Remote Sensing Letters, 19, Article 6012305, 1–5. https://doi.org/10.1109/LGRS.2022.3194257
  28. Ran, R., Deng, L.-J., Jiang, T.-X., Hu, J.-F., Chanussot, J., & Vivone, G. (2023). GuidedNet: A general CNN fusion framework via high-resolution guidance for hyperspectral image super-resolution. IEEE Transactions on Cybernetics, 53(7), 4148–4161. https://doi.org/10.1109/TCYB.2023.3238200
  29. Wang, X., Hu, Q., Cheng, Y., & Ma, J. (2023). Hyperspectral image super-resolution meets deep learning: A survey and perspective. IEEE/CAA Journal of Automatica Sinica, 10(8), 1668–1691. https://doi.org/10.1109/JAS.2023.123681
  30. Dian, R., Li, S., Sun, B., & Guo, A. (2021). Recent advances and new guidelines on hyperspectral and multispectral image fusion. Information Fusion, 69, 40–51. https://doi.org/10.1016/j.inffus.2020.11.001
  31. Mianji, F. A., Gu, Y., Zhang, Y., & Zhang, J. (2011). Enhanced self-training superresolution mapping technique for hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 8(4), 671–675. https://doi.org/10.1109/LGRS.2010.2102334
  32. Hu, J., Li, Y., & Xie, W. (2017). Hyperspectral image super-resolution by spectral difference learning and spatial error correction. IEEE Geoscience and Remote Sensing Letters, 14(10), 1825–1829. https://doi.org/10.1109/LGRS.2017.2737637
  33. Irmak, H., Akar, G. B., & Yuksel, S. E. (2018). A MAP-based approach for hyperspectral imagery super-resolution. IEEE Transactions on Image Processing, 27(6), 2942–2951. https://doi.org/10.1109/TIP.2018.2814210
  34. Jia, J., Ji, L., Zhao, Y., & Geng, X. (2018). Hyperspectral image super-resolution with spectral–spatial network. International Journal of Remote Sensing, 39(22), 7806–7829. https://doi.org/10.1080/01431161.2018.1471546
  35. 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.2017.2704122
  36. Li, Y., Hu, J., Zhao, X., Xie, W., & Li, J. (2017). Hyperspectral image super-resolution using deep convolutional neural network. Neurocomputing, 266, 29–41. https://doi.org/10.1016/j.neucom.2017.05.024
  37. Yuan, Y., Zheng, X., & Lu, X. (2017). Hyperspectral image superresolution by transfer learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 1963–1974. https://doi.org/10.1109/JSTARS.2017.2655112
  38. Arun, P. V., Buddhiraju, K. M., Porwal, A., & Chanussot, J. (2020). CNN-based super-resolution of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 58(9), 6106–6121. https://doi.org/10.1109/TGRS.2020.2973370
  39. Fu, Y., Liang, Z., & You, S. (2021). Bidirectional 3D quasi-recurrent neural network for hyperspectral image super-resolution. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 2674–2688. https://doi.org/10.1109/JSTARS.2021.3057936
  40. Jiang, J., Sun, H., Liu, X., & Ma, J. (2020). Learning spatial-spectral prior for super-resolution of hyperspectral imagery. IEEE Transactions on Computational Imaging, 6, 1082–1096. https://doi.org/10.1109/TCI.2020.2996075
  41. Li, J., Cui, R., Li, B., Song, R., Li, Y., Dai, Y., & Du, Q. (2020). Hyperspectral image super-resolution by band attention through adversarial learning. IEEE Transactions on Geoscience and Remote Sensing, 58(6), 4304–4318. https://doi.org/10.1109/TGRS.2019.2962713
  42. Li, Q., Wang, Q., & Li, X. (2020). Mixed 2D/3D convolutional network for hyperspectral image super-resolution. Remote Sensing, 12(10), 1660. https://doi.org/10.3390/rs12101660
  43. Li, Q., Wang, Q., & Li, X. (2021). Exploring the relationship between 2D/3D convolution for hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 59(10), 8693–8703. https://doi.org/10.1109/TGRS.2020.3047363
  44. Wang, Q., Li, Q., & Li, X. (2021). Hyperspectral image superresolution using spectrum and feature context. IEEE Transactions on Industrial Electronics, 68(11), 11276–11285. https://doi.org/10.1109/TIE.2020.3038096
  45. Wang, X., Ma, J., & Jiang, J. (2022). Hyperspectral image super-resolution via recurrent feedback embedding and spatial–spectral consistency regularization. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5503113, 1–13. https://doi.org/10.1109/TGRS.2021.3064450
  46. Zheng, K., Gao, L., Ran, Q., Cui, X., Zhang, B., Liao, W., & Jia, S. (2019). Separable-spectral convolution and inception network for hyperspectral image super-resolution. International Journal of Machine Learning and Cybernetics, 10, 2593–2607. https://doi.org/10.1007/s13042-018-00911-4
  47. Liu, Y., Hu, J., Kang, X., Luo, J., & Fan, S. (2022). Interactformer: Interactive transformer and CNN for hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5531715, 1–15. https://doi.org/10.1109/TGRS.2022.3183468
  48. Chen, S., Zhang, L., & Zhang, L. (2023). MSDformer: Multiscale deformable transformer for hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 61, Article 5525614, 1–14. https://doi.org/10.1109/TGRS.2023.3315970
  49. Zhang, M., Zhang, C., Zhang, Q., Guo, J., Gao, X., & Zhang, J. (2023). ESSAformer: Efficient transformer for hyperspectral image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 23016–23027). Paris, France. https://doi.org/10.1109/ICCV51070.2023.02109
  50. Chen, S., Zhang, L., & Zhang, L. (2024). Cross-scope spatial-spectral information aggregation for hyperspectral image super-resolution. IEEE Transactions on Image Processing, 33, 5878–5891. https://doi.org/10.1109/TIP.2024.3468905
  51. Hu, Q., Wang, X., Jiang, J., Zhang, X.-P., & Ma, J. (2024). Exploring the spectral prior for hyperspectral image super-resolution. IEEE Transactions on Image Processing, 33, 5260–5272. https://doi.org/10.1109/TIP.2024.3460470
  52. Yang, X., Li, S., Cao, W., Tang, D., Ban, Y., & Zhou, Y. (2026). RWKVSR: Receptance weighted key-value network for hyperspectral image super-resolution. IEEE Transactions on Circuits and Systems for Video Technology, 36(4), 4165–4180. https://doi.org/10.1109/TCSVT.2025.3626779
  53. Wu, C., Wang, D., Bai, Y., Mao, H., Li, Y., & Shen, Q. (2023). HSR-Diff: Hyperspectral image super-resolution via conditional diffusion models. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 7060–7070). Paris, France. https://doi.org/10.1109/ICCV51070.2023.00652
  54. Xu, Y., Wang, H., Zhou, F., Luo, C., Sun, X., Rahardja, S., & Ren, P. (2025). MambaHSISR: Mamba hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 63, Article 5511216, 1–16. https://doi.org/10.1109/TGRS.2025.3560632
  55. Su, X., Shen, X., Wan, M., Nie, J., Chen, L., Liu, H., & Zhou, X. (2025). EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-Resolution. Proceedings of the AAAI Conference on Artificial Intelligence, 39(7), 7033–7041. https://doi.org/10.1609/aaai.v39i7.32755
  56. Gendy, G., & AlMarzouqi, H. (2026). Remote sensing image super-resolution via hybrid Mamba and convolutional architectures. Neurocomputing, 683, 133561. https://doi.org/10.1016/j.neucom.2026.133561
  57. Cao, H., Tan, Z., Zhu, B., Xiong, H., & Lv, Q. (2026). A review on super-resolution reconstruction of single-frame remote sensing images via diffusion models. Remote Sensing, 18(11), 1702. https://doi.org/10.3390/rs18111702
  58. Yang, P., Ma, Y., Mei, X., Chen, Q., Wu, M., & Ma, J. (2025). Deep blind super-resolution for hyperspectral images. Pattern Recognition, 157, 110916. https://doi.org/10.1016/j.patcog.2024.110916
  59. Cheng, Y., Wang, X., Ma, Y., Mei, X., Wu, M., & Ma, J. (2025). General hyperspectral image super-resolution via meta-transfer learning. IEEE Transactions on Neural Networks and Learning Systems, 36(4), 6134–6147. https://doi.org/10.1109/TNNLS.2024.3387970
  60. Li, K., Van Gool, L., & Dai, D. (2025). Test-time training for hyperspectral image super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(9), 7231–7242. https://doi.org/10.1109/TPAMI.2024.3461807
  61. Chen, C., Wang, Y., Zhang, N., Zhang, Y., & Zhao, Z. (2023). A review of hyperspectral image super-resolution based on deep learning. Remote Sensing, 15(11), 2853. https://doi.org/10.3390/rs15112853
  62. Dong, W., Zhou, C., Wu, F., Wu, J., Shi, G., & Li, X. (2021). Model-guided deep hyperspectral image super-resolution. IEEE Transactions on Image Processing, 30, 5754–5768. https://doi.org/10.1109/TIP.2021.3078058
  63. Liu, D., Li, J., Yuan, Q., Zheng, L., He, J., Zhao, S., & Xiao, Y. (2023). An efficient unfolding network with disentangled spatial-spectral representation for hyperspectral image super-resolution. Information Fusion, 94, 92–111. https://doi.org/10.1016/j.inffus.2023.01.018
  64. Wang, K., Liao, X., Li, J., Meng, D., & Wang, Y. (2023). Hyperspectral image super-resolution via knowledge-driven deep unrolling and transformer embedded convolutional recurrent neural network. IEEE Transactions on Image Processing, 32, 4581–4594. https://doi.org/10.1109/TIP.2023.3293768
  65. Cao, X., Wang, X., Dun, X., Lian, Y., Cheng, X., & Hao, X. (2026). Cross-domain-aware deep unfolding transformer for hyperspectral image super-resolution. Pattern Recognition, 172(Part A), 112374. https://doi.org/10.1016/j.patcog.2025.112374
  66. Guo, W.-J., Xie, W., Jiang, K., Li, Y., Lei, J., & Fang, L. (2023). Toward stable, interpretable, and lightweight hyperspectral super-resolution. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 22272–22281). Vancouver, BC, Canada. https://doi.org/10.1109/CVPR52729.2023.02133
  67. Yang, J., Wu, C., You, T., Wang, D., Li, Y., Shang, C., & Shen, Q. (2023). Hierarchical spatio-spectral fusion for hyperspectral image super resolution via sparse representation and pre-trained deep model. Knowledge-Based Systems, 260, 110170. https://doi.org/10.1016/j.knosys.2022.110170
  68. Deka, B., Mullah, H. U., Barman, T., & Datta, S. (2023). Joint sparse representation-based single image super-resolution for remote sensing applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 2352–2365. https://doi.org/10.1109/JSTARS.2023.3244069
  69. Ye, F., Wu, Z., Xu, Y., Liu, H., & Wei, Z. (2022). Bayesian hyperspectral image super-resolution in the presence of spectral variability. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5545613, 1–13. https://doi.org/10.1109/TGRS.2022.3228313
  70. Muhammad, U., & Laaksonen, J. (2025). Hybrid deep learning for hyperspectral single-image super-resolution. IEEE Geoscience and Remote Sensing Letters, 22, Article 5509905, 1–5. https://doi.org/10.1109/LGRS.2025.3616772
  71. 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, Article 5504218, 1–18. https://doi.org/10.1109/TGRS.2025.3531646
  72. Aydilek, H., & İnanç, N. (2021). Learning Based Super Resolution Application for Hyperspectral Images. International Scientific and Vocational Studies Journal, 5(2), 210-217. https://doi.org/10.47897/bilmes.1049338
  73. Dong, C., Loy, C. C., He, K., & Tang, X. (2016). Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 295–307. https://doi.org/10.1109/TPAMI.2015.2439281
  74. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2261–2269). Honolulu, HI, United States. https://doi.org/10.1109/CVPR.2017.243
  75. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770–778). Las Vegas, NV, United States. https://doi.org/10.1109/CVPR.2016.90
  76. Wen, R., Fu, K., Sun, H., Sun, X., & Wang, L. (2018). Image superresolution using densely connected residual networks. IEEE Signal Processing Letters, 25(10), 1565–1569. https://doi.org/10.1109/LSP.2018.2861989
  77. Elad, M. (2010). Sparse and redundant representations: From theory to applications in signal and image processing. Springer. https://doi.org/10.1007/978-1-4419-7011-4
  78. Yasuma, F., Mitsunaga, T., Iso, D., & Nayar, S. K. (2010). Generalized assorted pixel camera: Postcapture control of resolution, dynamic range, and spectrum. IEEE Transactions on Image Processing, 19(9), 2241–2253. https://doi.org/10.1109/TIP.2010.2046811
  79. Chakrabarti, A., & Zickler, T. (2011). Statistics of real-world hyperspectral images. In CVPR 2011 (pp. 193–200). Colorado Springs, CO, United States. https://doi.org/10.1109/CVPR.2011.5995660
  80. Girouard, G., Bannari, A., El Harti, A., & Desrochers, A. (2004). Validated spectral angle mapper algorithm for geological mapping: Comparative study between QuickBird and Landsat-TM. In Proceedings of the 20th ISPRS Congress: Geo-Imagery Bridging Continents (pp. 599–604). Istanbul, Turkey.