Spatio-temporal super-resolution for multi-videos based on belief propagation

Zhang, Tinghua; Gao, Kun; Ni, Guoqiang; Fan, Guihua; Lu, Yan

doi:10.1016/j.image.2018.07.002

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…After that, you could perform superresolution reconstruction using the network model you learned before. Literature [27] built a Markov random field using the maximum posterior probability and then used the belief propagation technique to maximize the estimate of model parameters. This enhanced the rebuilt image's edge sharpness and texture details.…”

Section: Related Workmentioning

confidence: 99%

Superresolution Reconstruction of Remote Sensing Image Based on Generative Adversarial Network

Zhou¹

2022

Wireless Communications and Mobile Computing

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To recreate high-resolution, more detailed remote sensing images from existing low-resolution photos, this technique is known as remote sensing image superresolution reconstruction, and it has numerous uses. As an important research hotspot of neural networks, generative adversarial network (GAN) has made outstanding progress for image superresolution reconstruction. It solves the computational complexity and low reconstructed image quality of standard superresolution reconstruction algorithms. This research offers a superresolution reconstruction strategy with a self-attention generative adversarial network to improve the quality of reconstructed superresolution remote sensing images. The self-attention strategy as well as residual module is utilized to build a generator in this model that transforms low-resolution remote sensing images into superresolution ones. It aims to determine the discrepancy between a reconstructed picture and a true picture by using a deep convolutional network as a discriminator. For the purpose of enhancing the accuracy, content loss is used. This is done to obtain accurate detail reconstruction. According to the findings of the experiments, this approach is capable of regenerating higher-quality images.

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Section: Related Workmentioning

confidence: 99%

Superresolution Reconstruction of Remote Sensing Image Based on Generative Adversarial Network

Zhou¹

2022

Wireless Communications and Mobile Computing

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“…12 In recent years, the methods based on sparse representation have been used for the echocardiography temporal SR based on the post-processing techniques. 13,14 In recent times, some studies have used a combination of temporal and spatial information in a variety of ways for video SR. [15][16][17][18][19] Deep learning-based video SR methods are the topics of interest in this field, which have promising performances. For example, a novel fast spatio-temporal residual network (FSTRN) is offered to adopt 3D convolutions for the temporal SR task on natural images.…”

Section: Introductionmentioning

confidence: 99%

“…Their experimental results show that the performance of this method is better than existing SR reconstruction methods in terms of accuracy and visual improvements. Another study 18 proposed the Maximum Posterior Likelihood-Markov Random Field (MAP-MRF)-based spatio-temporal SR reconstruction method for SR of the real-world imaging. In another novel study, a new model based on optical flow and Zernike moment is proposed for spatio-temporal SR reconstruction.…”

Section: Introductionmentioning

confidence: 99%

“…According to their experimental results, this model outperforms the existing video SR methods. However, all of these studies have been performed on natural images, and they are based on post-processing approaches in temporal SR. [15][16][17][18][19] To the best of our knowledge, there is no such a study on medical images, in which the combination of temporal and spatial information for the temporal SR using the post-processing approaches has been investigated.…”

Section: Introductionmentioning

confidence: 99%

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Temporal Super-resolution of Ultrasound Imaging Using Matrix Completion

2020

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The temporal super-resolution of the dynamic ultrasound imaging, a means to observe rapid heart movements, is considered an important subject in medical diagnosis of cardiac conditions. Here, a new technique based on the acquisition scheme using the matrix completion (MC) theory is offered for the temporal super-resolution of the two-dimensional (2D) and three-dimensional (3D) ultrasound imaging. MC mentions the problem of completing a low-rank matrix when only a subset of its elements can be observed. Here, the lower scan lines are acquired. Whereby, the proposed method uses temporal and spatial information of the radio frequency (RF) image sequences for the reconstruction of skipped RF lines. This is performed using the construction of the MC images and then reconstruction of them by the MC theory. The results of the proposed method are compared with the compressive sensing (CS) reconstruction methods. The qualitative and quantitative evaluations of 2D and 3D data demonstrate that in the proposed method, which uses the spatial and temporal relation of RF images and the MC theory, the reconstruction is more accurate, and the reconstruction error is lower. The computational complexity of this method is very low. It also does not require hardware adjustments. Therefore, it can be easily implemented in current ultrasound-imaging devices with the frame-rate enhancement. For instance, the frame rate up to two times the original sequence is feasible using the proposed methods, while root mean square error is decreased by about 35% and 30% for 2D and 3D data, respectively, compared with the CS reconstruction method.

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