Lightweight Feedback Convolution Neural Network for Remote Sensing Images Super-Resolution

Jin, Wang; Wu, Yiming; Wang, Liu; Wang, Lei; Alfarraj, Osama; Tolba, Amr

doi:10.1109/access.2021.3052946

Cited by 9 publications

(3 citation statements)

References 43 publications

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“…In the Ref. [44], the authors address the heavy computation and the high number of parameters of current SR techniques. They suggest the feedback ghost residual dense network (FGRDN).…”

Section: Deep Learning and Super-resolution (Sr) Techniquesmentioning

confidence: 99%

A Novel Implicit Neural Representation for Volume Data

Sheibanifard

2023

Applied Sciences

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The storage of medical images is one of the challenges in the medical imaging field. There are variable works that use implicit neural representation (INR) to compress volumetric medical images. However, there is room to improve the compression rate for volumetric medical images. Most of the INR techniques need a huge amount of GPU memory and a long training time for high-quality medical volume rendering. In this paper, we present a novel implicit neural representation to compress volume data using our proposed architecture, that is, the Lanczos downsampling scheme, SIREN deep network, and SRDenseNet high-resolution scheme. Our architecture can effectively reduce training time, and gain a high compression rate while retaining the final rendering quality. Moreover, it can save GPU memory in comparison with the existing works. The experiments show that the quality of reconstructed images and training speed using our architecture is higher than current works which use the SIREN only. Besides, the GPU memory cost is evidently decreased.

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“…In the Ref. [44], the authors address the heavy computation and the high number of parameters of current SR techniques. They suggest the feedback ghost residual dense network (FGRDN).…”

Section: Deep Learning and Super-resolution (Sr) Techniquesmentioning

confidence: 99%

A Novel Implicit Neural Representation for Volume Data

Sheibanifard

2023

Applied Sciences

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“…Therefore, a feedback ghost residual dense network (FGRDN) is proposed. The convolution of residual dense blocks (RDBs) is replaced by ghost modules (GMs) in this network, which avoids parameter increase, converges parameters quickly, and improves network performance [9]. With the development of science and technology, UAV is no longer simply used for entertainment but has been used in various studies.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Water Body Recognition Algorithm Based on Deep Convolution Generating Network and Combined Features

Zhao

Feng

2022

Wireless Communications and Mobile Computing

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The application of remote sensing images in water body recognition has become an effective method for ecological environment detection and evaluation, which has the disadvantages of low efficiency due to the existence of interpretation marks and rich interpretation experience in the current water body environment recognition, and overreliance on human experience. In this paper, the water body recognition method is applied to remote sensing images by combining the deep convolution generation network and the combined features, which has the advantage of high recognition accuracy. In the convolutional neural network, a five-layer convolutional neural network is used to construct a remote sensing water information extraction model, the transfer learning idea is introduced, and the densely connected feature fusion structure is added, so as to achieve the purposes of accelerating the convergence speed of the neural network, reducing the requirements of the neural network on the scale of training data, and reducing the loss of spatial hierarchical information and small object information. Compared with SVM, DBN, and CNN models, the experimental results show that the recognition accuracy of the proposed method is as high as 95. 69% under the constraint of scale window, which has a wide range of application scenarios and practical significance.

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“…Single image super-resolution (SISR) is a kind of low-level and ill-posed image processing problem [1][2][3][4][5]. Since real low-resolution (LR) images have innumerable high-resolution (HR) images corresponding to them, traditional interpolation methods cannot extract highfrequency information from low-resolution images, resulting in the failure to obtain pleasant visual effect.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Single Image Super-Resolution Convolution Neural Network in Portable Device

2021

KSII TIIS

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Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important Wang et al.: Lightweight Single Image Super-Resolution Convolution Neural Network in Portable Deviceinformation extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.

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Lightweight Feedback Convolution Neural Network for Remote Sensing Images Super-Resolution

Cited by 9 publications

References 43 publications

A Novel Implicit Neural Representation for Volume Data

A Novel Implicit Neural Representation for Volume Data

Remote Sensing Image Water Body Recognition Algorithm Based on Deep Convolution Generating Network and Combined Features

Lightweight Single Image Super-Resolution Convolution Neural Network in Portable Device

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