Remote sensing image reconstruction using an asymmetric multi-scale super-resolution network

Huan, Hai; Zou, Nan; Zhang, Yi; Xie, Yaqin; Wang, Chao

doi:10.1007/s11227-022-04617-x

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…The convolutional neural network EDSR [31] for super-resolution reconstruction was subjected to some modifications to enhance the efficiency and scalability of PAI reconstruction. Deep learning-based super-resolution reconstruction algorithms have been used to obtain high-resolution (HR) images from their low-resolution (LR) counterparts in various fields [38,39]. The EDSR network, as a deep-learning model for image super-resolution reconstruction, employs techniques such as residual learning and dense connectivity by increasing the depth and the number of parameters of the network, which can efficiently improve the quality of image super-resolution reconstruction and accuracy.…”

Section: Deep-learning Algorithms For Pai Reconstructionmentioning

confidence: 99%

Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images

Jiang,

He,

Chen

et al. 2024

Applied Sciences

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Photoacoustic imaging (PAI) is an emerging imaging technique that offers real-time, non-invasive, and radiation-free measurements of optical tissue properties. However, image quality degradation due to factors such as non-ideal signal detection hampers its clinical applicability. To address this challenge, this paper proposes an algorithm for super-resolution reconstruction and segmentation based on deep learning. The proposed enhanced deep super-resolution minimalistic network (EDSR-M) not only mitigates the shortcomings of the original algorithm regarding computational complexity and parameter count but also employs residual learning and attention mechanisms to extract image features and enhance image details, thereby achieving high-quality reconstruction of PAI. DeepLabV3+ is used to segment the images before and after reconstruction to verify the network reconstruction performance. The experimental results demonstrate average improvements of 19.76% in peak-signal-to-noise ratio (PSNR) and 4.80% in structural similarity index (SSIM) for the reconstructed images compared to those of their pre-reconstructed counterparts. Additionally, mean accuracy, mean intersection and union ratio (IoU), and mean boundary F1 score (BFScore) for segmentation showed enhancements of 8.27%, 6.20%, and 6.28%, respectively. The proposed algorithm enhances the effect and texture features of PAI and makes the overall structure of the image restoration more complete.

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Section: Deep-learning Algorithms For Pai Reconstructionmentioning

confidence: 99%

Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images

Jiang,

He,

Chen

et al. 2024

Applied Sciences

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SDPH: a new technique for spatial detection of path holes from huge volume high-resolution raster images in near real-time

Tasyurek

2024

J Real-Time Image Proc

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Detecting and repairing road defects is crucial for road safety, vehicle maintenance, and enhancing tourism on well-maintained roads. However, monitoring all roads by vehicle incurs high costs. With the widespread use of remote sensing technologies, high-resolution satellite images offer a cost-effective alternative. This study proposes a new technique, SDPH, for automated detection of damaged roads from vast, high-resolution satellite images. In the SDPH technique, satellite images are organized in a pyramid grid file system, allowing deep learning methods to efficiently process them. The images, generated as $$256\times 256$$ 256 × 256 dimensions, are stored in a directory with explicit location information. The SDPH technique employs a two-stage object detection models, utilizing classical and modified RCNNv3, YOLOv5, and YOLOv8. Classical RCNNv3, YOLOv5, and YOLOv8 and modified RCNNv3, YOLOv5, and YOLOv8 in the first stage for identifying roads, achieving f1 scores of 0.743, 0.716, 0.710, 0.955, 0.958, and 0.954, respectively. When the YOLOv5, with the highest f1 score, was fed to the second stage; modified RCNNv3, YOLOv5, and YOLOv8 detected road defects, achieving f1 scores of 0.957,0.971 and 0.964 in the second process. When the same CNN model was used for road and road defect detection in the proposed SDPH model, classical RCNNv3, improved RCNNv3, classical YOLOv5, improved YOLOv5, classical YOLOv8, improved RCNNv8 achieved micro f1 scores of 0.752, 0.956, 0.726, 0.969, 0.720 and 0.965, respectively. In addition, these models processed 11, 10, 33, 31, 37, and 36 FPS images by performing both stage operations, respectively. Evaluations on geotiff satellite images from Kayseri Metropolitan Municipality, ranging between 20 and 40 gigabytes, demonstrated the efficiency of the SDPH technique. Notably, the modified YOLOv5 outperformed, detecting paths and defects in 0.032 s with the micro f1 score of 0.969. Fine-tuning on TileCache enhanced f1 scores and reduced computational costs across all models.

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MMDAN: multiwavelet based multiscale dilated attention network for remote sensing image super-resolution

Sandana,

Deivalakshmi,

George

2024

SIViP

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Remote sensing image reconstruction using an asymmetric multi-scale super-resolution network

Cited by 6 publications

References 36 publications

Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images

Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images

SDPH: a new technique for spatial detection of path holes from huge volume high-resolution raster images in near real-time

MMDAN: multiwavelet based multiscale dilated attention network for remote sensing image super-resolution

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