Research Article

Modified very deep super resolution network - Gaussian mixture models based single image super-resolution: A comprehensive comparative analysis

Volume: 8 July 3, 2026

Modified very deep super resolution network - Gaussian mixture models based single image super-resolution: A comprehensive comparative analysis

Abstract

Image Super-Resolution (SISR) is a vital, cost-effective solution for enhancing the spatial resolution of satellite imagery. However, a significant challenge in current SISR techniques is that while deep learning models excel at structural learning, they often struggle to accurately reconstruct intricate high-frequency details and preserve the statistical texture properties of high-resolution images. To address this gap, this paper proposes a novel hybrid method, termed mVDSR-GMM, which leverages the complementary strengths of Gaussian Mixture Models (GMM) and a modified Very Deep Super-Resolution (mVDSR) network. GMM is utilized to model the underlying statistical and probabilistic properties of the image for accurate high-frequency reconstruction while the mVDSR network enhanced with 25 convolutional layers and Leaky ReLU activations boosts the model’s structural feature extraction and non-linear mapping capabilities. This hybrid approach technically integrates the mVDSR and GMM through a parallel merging of their estimated residuals within the luminance channel, thereby enhancing the spatial reconstruction of satellite imagery. The enhanced mVDSR network, with increased depth and Leaky ReLU activation functions, further boosts the model's learning capability and robustness. Through extensive evaluations using satellite imagery datasets and metrics such as PSNR, SSIM, BRISQUE, MSE, UIQI, SAM, and FD, mVDSR-GMM consistently outperformed existing techniques, demonstrating its effectiveness in improving spatial resolution. Experimental results indicate that mVDSR-GMM outperforms main competitors such as SwinIR and SinSR across all evaluation metrics, providing a robust reconstruction performance that is further validated by improved accuracy in the binary separation of urban structures (buildings and roads) from natural environments. The proposed mVDSR-GMM emerges as a promising tool for advancing satellite imagery resolution, with potential applications in remote sensing and geospatial analysis.

Keywords

References

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Details

Primary Language

English

Subjects

Image Processing, Geoscience Data Visualisation

Journal Section

Research Article

Publication Date

July 3, 2026

Submission Date

November 25, 2025

Acceptance Date

January 28, 2026

Published in Issue

Year 2026 Volume: 8

APA
Günen, M. A. (2026). Modified very deep super resolution network - Gaussian mixture models based single image super-resolution: A comprehensive comparative analysis. Turkish Journal of Remote Sensing, 8. https://doi.org/10.51489/tuzal.1829904
AMA
1.Günen MA. Modified very deep super resolution network - Gaussian mixture models based single image super-resolution: A comprehensive comparative analysis. TJRS. 2026;8. doi:10.51489/tuzal.1829904
Chicago
Günen, Mehmet Akıf. 2026. “Modified Very Deep Super Resolution Network - Gaussian Mixture Models Based Single Image Super-Resolution: A Comprehensive Comparative Analysis”. Turkish Journal of Remote Sensing 8 (July). https://doi.org/10.51489/tuzal.1829904.
EndNote
Günen MA (July 1, 2026) Modified very deep super resolution network - Gaussian mixture models based single image super-resolution: A comprehensive comparative analysis. Turkish Journal of Remote Sensing 8
IEEE
[1]M. A. Günen, “Modified very deep super resolution network - Gaussian mixture models based single image super-resolution: A comprehensive comparative analysis”, TJRS, vol. 8, July 2026, doi: 10.51489/tuzal.1829904.
ISNAD
Günen, Mehmet Akıf. “Modified Very Deep Super Resolution Network - Gaussian Mixture Models Based Single Image Super-Resolution: A Comprehensive Comparative Analysis”. Turkish Journal of Remote Sensing 8 (July 1, 2026). https://doi.org/10.51489/tuzal.1829904.
JAMA
1.Günen MA. Modified very deep super resolution network - Gaussian mixture models based single image super-resolution: A comprehensive comparative analysis. TJRS. 2026;8. doi:10.51489/tuzal.1829904.
MLA
Günen, Mehmet Akıf. “Modified Very Deep Super Resolution Network - Gaussian Mixture Models Based Single Image Super-Resolution: A Comprehensive Comparative Analysis”. Turkish Journal of Remote Sensing, vol. 8, July 2026, doi:10.51489/tuzal.1829904.
Vancouver
1.Mehmet Akıf Günen. Modified very deep super resolution network - Gaussian mixture models based single image super-resolution: A comprehensive comparative analysis. TJRS. 2026 Jul. 1;8. doi:10.51489/tuzal.1829904

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