SR-DeblurUGAN: An End-to-End Super-Resolution and Deblurring Model with High Performance

Xiao, Yuzhen; Zhang, Jidong; Chen, Wei; Wang, Yichen; You, Jianing; Wang, Qing

doi:10.3390/drones6070162

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…Correspondingly, WRA-Net-Wide Receptive Field Attention Network, see [92]-was introduced to deblur the motion-blurred images, which improved the crop weed segmentation accuracy. Moreover, Xiao et al [93] introduced a novel hybrid technique, namely SR-DeblurUGAN, encompassing both image deblurring and super-resolution, which gained a stable performance on agricultural drone image enhancement.…”

Section: Deep Neural Network and Generative Adversarial Networkmentioning

confidence: 99%

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

Modak,

Heil,

Stein

2024

Remote Sensing

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Image preprocessing and fusion are commonly used for enhancing remote-sensing images, but the resulting images often lack useful spatial features. As the majority of research on image fusion has concentrated on the satellite domain, the image-fusion task for Unmanned Aerial Vehicle (UAV) images has received minimal attention. This study investigated an image-improvement strategy by integrating image preprocessing and fusion tasks for UAV images. The goal is to improve spatial details and avoid color distortion in fused images. Techniques such as image denoising, sharpening, and Contrast Limited Adaptive Histogram Equalization (CLAHE) were used in the preprocessing step. The unsharp mask algorithm was used for image sharpening. Wiener and total variation denoising methods were used for image denoising. The image-fusion process was conducted in two steps: (1) fusing the spectral bands into one multispectral image and (2) pansharpening the panchromatic and multispectral images using the PanColorGAN model. The effectiveness of the proposed approach was evaluated using quantitative and qualitative assessment techniques, including no-reference image quality assessment (NR-IQA) metrics. In this experiment, the unsharp mask algorithm noticeably improved the spatial details of the pansharpened images. No preprocessing algorithm dramatically improved the color quality of the enhanced images. The proposed fusion approach improved the images without importing unnecessary blurring and color distortion issues.

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Section: Deep Neural Network and Generative Adversarial Networkmentioning

confidence: 99%

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

Modak,

Heil,

Stein

2024

Remote Sensing

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“…The UNET model has shown outstanding performance in image segmentation tasks, particularly in tasks requiring precise detail segmentation. In recent years, experts have attempted to introduce it into the field of image super-resolution with good results [35][36][37][38]. The traditional UNET architecture efficiently extracts multi-scale features through design, consisting of symmetric branches for encoding and decoding.…”

Section: Structure Of the Modelmentioning

confidence: 99%

Enhanced Wind Field Spatial Downscaling Method Using UNET Architecture and Dual Cross-Attention Mechanism

Liu,

Shi,

et al. 2024

Remote Sensing

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Before 2008, China lacked high-coverage regional surface observation data, making it difficult for the China Meteorological Administration Land Data Assimilation System (CLDAS) to directly backtrack high-resolution, high-quality land assimilation products. To address this issue, this paper proposes a deep learning model named UNET_DCA, based on the UNET architecture, which incorporates a Dual Cross-Attention module (DCA) for multiscale feature fusion by introducing Channel Cross-Attention (CCA) and Spatial Cross-Attention (SCA) mechanisms. This model focuses on the near-surface 10-meter wind field and achieves spatial downscaling from 6.25 km to 1 km. We conducted training and validation using data from 2020–2021, tested with data from 2019, and performed ablation experiments to validate the effectiveness of each module. We compared the results with traditional bilinear interpolation methods and the SNCA-CLDASSD model. The experimental results show that the UNET-based model outperforms SNCA-CLDASSD, indicating that the UNET-based model captures richer information in wind field downscaling compared to SNCA-CLDASSD, which relies on sequentially stacked CNN convolution modules. UNET_CCA and UNET_SCA, incorporating cross-attention mechanisms, outperform UNET without attention mechanisms. Furthermore, UNET_DCA, incorporating both Channel Cross-Attention and Spatial Cross-Attention mechanisms, outperforms UNET_CCA and UNET_SCA, which only incorporate one attention mechanism. UNET_DCA performs best on the RMSE, MAE, and COR metrics (0.40 m/s, 0.28 m/s, 0.93), while UNET_DCA_ars, incorporating more auxiliary information, performs best on the PSNR and SSIM metrics (29.006, 0.880). Evaluation across different methods indicates that the optimal model performs best in valleys, followed by mountains, and worst in plains; it performs worse during the day and better at night; and as wind speed levels increase, accuracy decreases. Overall, among various downscaling methods, UNET_DCA and UNET_DCA_ars effectively reconstruct the spatial details of wind fields, providing a deeper exploration for the inversion of high-resolution historical meteorological grid data.

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“…The smooth l_1 loss is also utilized in this process. In contrast, Xiao et al [34] proposed a two-stage image quality improvement model that first employs super-resolution using SRGAN, followed by correction and deblurring using a UNet-GAN model. Similarly, Li et al [35] suggested a super-resolution model based on a GAN to improve UAV detection.…”

Section: Related Workmentioning

confidence: 99%

TESR: Two-Stage Approach for Enhancement and Super-Resolution of Remote Sensing Images

et al. 2023

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Remote Sensing (RS) images are usually captured at resolutions lower than those required. Deep Learning (DL)-based super-resolution (SR) architectures are typically used to increase the resolution artificially. In this study, we designed a new architecture called TESR (Two-stage approach for Enhancement and super-resolution), leveraging the power of Vision Transformers (ViT) and the Diffusion Model (DM) to increase the resolution of RS images artificially. The first stage is the ViT-based model, which serves to increase resolution. The second stage is an iterative DM pre-trained on a larger dataset, which serves to increase image quality. Every stage is trained separately on the given task using a separate dataset. The self-attention mechanism of the ViT helps the first stage generate global and contextual details. The iterative Diffusion Model helps the second stage enhance the image’s quality and generate consistent and harmonic fine details. We found that TESR outperforms state-of-the-art architectures on super-resolution of remote sensing images on the UCMerced benchmark dataset. Considering the PSNR/SSIM metrics, TESR improves SR image quality as compared to state-of-the-art techniques from 34.03/0.9301 to 35.367/0.9449 in the scale ×2. On a scale of ×3, it improves from 29.92/0.8408 to 32.311/0.91143. On a scale of ×4, it improves from 27.77/0.7630 to 31.951/0.90456. We also found that the Charbonnier loss outperformed other loss functions in the training of both stages of TESR. The improvement was by a margin of 21.5%/14.3%, in the PSNR/SSIM, respectively. The source code of TESR is open to the community.

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SR-DeblurUGAN: An End-to-End Super-Resolution and Deblurring Model with High Performance

Cited by 7 publications

References 34 publications

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

Enhanced Wind Field Spatial Downscaling Method Using UNET Architecture and Dual Cross-Attention Mechanism

TESR: Two-Stage Approach for Enhancement and Super-Resolution of Remote Sensing Images

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