A novel lossless compression encoding framework for SAR remote sensing images

Fan, Chunxiao; Hu, Ziqi; Jia, Lu; Min, Hai

doi:10.1007/s11760-020-01763-8

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…The feature map F is then subjected to the average pooling operation with the kernel size of (5, 9, 17), the step size of (2,4,8), and padding of (2,4,8). The output is then subjected to 1 × 1 convolutional layer dimensionality reduction and upsampling operation to change the size of the feature map, yielding…”

Section: (B)mentioning

confidence: 99%

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BFBE-Net: deep bilateral fusion and bilateral embedded network for real-time semantic segmentation

Hou,

Cheng,

Dai

et al. 2023

J. Electron. Imag.

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Real-time semantic segmentation has been challenging, and the fusion of features from different branches remains crucial to improvement. The two-branch structure has shown promising results in real-time semantic segmentation. However, upsampling feature maps from the semantic branch to match the detail branch leads to a loss of object feature information and compromises segmentation accuracy. We propose a deep bilateral fusion and bilateral embedded network (BFBE-Net) based on the encoder-decoder structure for real-time semantic segmentation to address these issues. The BFBE-Net adopts a two-branch design in the encoder, with a top-down fusion module and a bottom-up fusion module designed to integrate multi-scale context information in the channel dimension, and assigns different weights to detailed information and semantic information to enhance information characteristics. In the decoder, a bilateral embedded attention module under the guidance of spatial and channel attention integrates semantic and spatial features, gradually upsampling feature maps to reduce the loss of feature information. In addition, an enhanced aggregation pyramid pooling module is designed to efficiently extract contextual information by combining depth-wise asymmetric convolution. The proposed algorithm is evaluated on two benchmark datasets, Cityscapes and CamVid, achieving 78.5% mean intersection over union (mIoU) at 82 frames per second (FPS) on the Cityscapes test set and 79.2% mIoU at 131 FPS on the CamVid test set. The proposed BFBE-Net not only improves segmentation accuracy but also ensures real-time performance.

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Section: (B)mentioning

confidence: 99%

“…Semantic segmentation is a fundamental task in computer vision that aims to assign a label to each pixel in an image, marking different types of objects with different colors. Its wide range of applications, including automatic driving, 1 remote sensing, 2 and medical imaging, 3 makes it a hot research topic.…”

Section: Introductionmentioning

confidence: 99%

BFBE-Net: deep bilateral fusion and bilateral embedded network for real-time semantic segmentation

Hou,

Cheng,

Dai

et al. 2023

J. Electron. Imag.

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“…Moreover, a novel lossless compression for SAR remote sensing images has been produced by Fan, C. et al [14]. They have discussed the problem of the large size of remote sensing images focusing on SAR (syntheticaperture radar) which usually requires highly efficient devices able to store and transfer images.…”

Section: Related Workmentioning

confidence: 99%

Semi-lossless Fractal MRI Image Compression Based on Fixed length Technique

Al-Salamee

Al‐Shammary

2021

J. Al-Qadisiyah Comp. Sci. Math.

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Medical image compression plays an essential role to handle large amounts of data for communication and storage purposes. Fractal image compression is a potential lossy compression models with a resulting image that loses some of its information. However, health data communication usually cannot afford any lose for patients visual information. This paper proposes a new high efficiency semi-lossless fractal image compression method (SLFIC) based on fractal theory and fixed length technique. Technically, the resultant lossy fractals compressed image is analyzed and error in comparison with the original image is detected. Then, Fixed-length is developed to compress the detected errors and attached to the compressed image. In practice, a potential performance by the new developed model has been obtained in comparison with two other lossless models: ( Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA) with Linde–Buzo–Gray (LBG) (L2-LBG)) and(Neural Network Radial Basis Function (NNRBF)). Moreover, a high quality that has been obtained by the proposed system based on Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).

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