A Study on an Improved Generative Adversarial Network Model for Super-Resolution Image Reconstruction
The SRGAN model based on Generative Adversarial Networks (GANs) has achieved breakthroughs in perceptual quality for image super-resolution reconstruction. However, existing models suffer from feature smoothing effects in the generator and unstable training of the discriminator. To address these lim...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11075683/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850101699231875072 |
|---|---|
| author | Ximin Qu Cheng Peng |
| author_facet | Ximin Qu Cheng Peng |
| author_sort | Ximin Qu |
| collection | DOAJ |
| description | The SRGAN model based on Generative Adversarial Networks (GANs) has achieved breakthroughs in perceptual quality for image super-resolution reconstruction. However, existing models suffer from feature smoothing effects in the generator and unstable training of the discriminator. To address these limitations, we propose an improved SRGAN model (denoted as SRGAN-GBDB) through multi-strategy collaborative optimization to improve reconstruction quality. The specific improvements include: Firstly, in the generator, we remove batch normalization (BN) layers to alleviate feature degradation and embed the Convolutional Block Attention Module (CBAM) to enhance high-frequency feature modeling. Secondly, we implement spectral normalization for the discriminator and incorporate spectrally normalized residual blocks into the discriminator to strengthen its discriminative capability. Additionally, the joint perceptual-adversarial-content loss function of the SRGAN model balances reconstruction accuracy and visual quality. Experimental results show that the optimized model SRGAN-GBDB outperforms the traditional Bicubic interpolation method, SRGAN, and several other existing super-resolution algorithms on the Set5, Set14, BSD100, Urban100, and DIV2K datasets. It not only improves quantitative metrics such as PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) but also demonstrates superior visual quality through better detail recovery and fewer artifacts, validating the effectiveness and superiority of the optimization strategy. Future work will explore more efficient optimization methods and lightweight network architectures to facilitate the application of image super-resolution systems in mobile platforms and specialized fields like remote sensing and medical imaging. |
| format | Article |
| id | doaj-art-fa8ac6da61734c649f504c7355f3c3ce |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fa8ac6da61734c649f504c7355f3c3ce2025-08-20T02:39:56ZengIEEEIEEE Access2169-35362025-01-011311975711977110.1109/ACCESS.2025.358720111075683A Study on an Improved Generative Adversarial Network Model for Super-Resolution Image ReconstructionXimin Qu0https://orcid.org/0009-0008-7998-2700Cheng Peng1https://orcid.org/0000-0003-0619-2771School of Computer Science and Technology, Xinjiang Normal University, Ürümqi, ChinaSchool of Computer Science and Technology, Xinjiang Normal University, Ürümqi, ChinaThe SRGAN model based on Generative Adversarial Networks (GANs) has achieved breakthroughs in perceptual quality for image super-resolution reconstruction. However, existing models suffer from feature smoothing effects in the generator and unstable training of the discriminator. To address these limitations, we propose an improved SRGAN model (denoted as SRGAN-GBDB) through multi-strategy collaborative optimization to improve reconstruction quality. The specific improvements include: Firstly, in the generator, we remove batch normalization (BN) layers to alleviate feature degradation and embed the Convolutional Block Attention Module (CBAM) to enhance high-frequency feature modeling. Secondly, we implement spectral normalization for the discriminator and incorporate spectrally normalized residual blocks into the discriminator to strengthen its discriminative capability. Additionally, the joint perceptual-adversarial-content loss function of the SRGAN model balances reconstruction accuracy and visual quality. Experimental results show that the optimized model SRGAN-GBDB outperforms the traditional Bicubic interpolation method, SRGAN, and several other existing super-resolution algorithms on the Set5, Set14, BSD100, Urban100, and DIV2K datasets. It not only improves quantitative metrics such as PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) but also demonstrates superior visual quality through better detail recovery and fewer artifacts, validating the effectiveness and superiority of the optimization strategy. Future work will explore more efficient optimization methods and lightweight network architectures to facilitate the application of image super-resolution systems in mobile platforms and specialized fields like remote sensing and medical imaging.https://ieeexplore.ieee.org/document/11075683/Optimization strategyimage super resolutionattention mechanismspectral normalizationresidual block |
| spellingShingle | Ximin Qu Cheng Peng A Study on an Improved Generative Adversarial Network Model for Super-Resolution Image Reconstruction IEEE Access Optimization strategy image super resolution attention mechanism spectral normalization residual block |
| title | A Study on an Improved Generative Adversarial Network Model for Super-Resolution Image Reconstruction |
| title_full | A Study on an Improved Generative Adversarial Network Model for Super-Resolution Image Reconstruction |
| title_fullStr | A Study on an Improved Generative Adversarial Network Model for Super-Resolution Image Reconstruction |
| title_full_unstemmed | A Study on an Improved Generative Adversarial Network Model for Super-Resolution Image Reconstruction |
| title_short | A Study on an Improved Generative Adversarial Network Model for Super-Resolution Image Reconstruction |
| title_sort | study on an improved generative adversarial network model for super resolution image reconstruction |
| topic | Optimization strategy image super resolution attention mechanism spectral normalization residual block |
| url | https://ieeexplore.ieee.org/document/11075683/ |
| work_keys_str_mv | AT ximinqu astudyonanimprovedgenerativeadversarialnetworkmodelforsuperresolutionimagereconstruction AT chengpeng astudyonanimprovedgenerativeadversarialnetworkmodelforsuperresolutionimagereconstruction AT ximinqu studyonanimprovedgenerativeadversarialnetworkmodelforsuperresolutionimagereconstruction AT chengpeng studyonanimprovedgenerativeadversarialnetworkmodelforsuperresolutionimagereconstruction |