Particle filter based multi-frame image super resolution

Ghasemi-Falavarjani, Negin; Moallem, Payman; Rahimi, Akbar

doi:10.1007/s11760-022-02406-w

Cited by 1 publication

(2 citation statements)

References 26 publications

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“…The network used three convolutional layers to represent different non-linear mapping relationships for reconstruction, and, although the network layers were shallow, they showed good performance at the time compared to traditional methods. Since then, a large number of deep learning-based methods have started to flood into super-resolution reconstruction techniques [16][17][18][19][20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

“…How to make full use of the high-low resolution information between frames is the key to video super-resolution reconstruction. Most of the existing video super-resolution algorithms [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] use the optical flow method for inter-frame alignment. These algorithms can handle the inter-frame information of the video but cannot make full use of the interframe information, especially in scenes with large inter-frame motion variations and distant targets, and there is still a need to continue to investigate how to perform better motion estimation and compensation.…”

Section: Introductionmentioning

confidence: 99%

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Video Super-Resolution Network with Gated High-Low Resolution Frames

Ouyang

2023

Applied Sciences

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In scenes with large inter-frame motion variations, distant targets, and blurred targets, the lack of inter-frame alignment can greatly affect the effectiveness of subsequent video super-resolution reconstruction. How to perform inter-frame alignment in such scenes is the key to super-resolution reconstruction. In this paper, a new motion compensation method is proposed to design an alignment network based on gated high-low resolution frames. The core idea is to introduce a gating mechanism while using the information of high-low resolution neighboring frames to perform motion compensation adaptively. Meanwhile, within this alignment framework, we further introduce a pre-initial hidden state network and a local scale hierarchical salient feature fusion network. The pre-initial hidden state network is mainly used to reduce the impact of unbalanced quality effects between frames that occur in one-way cyclical networks; the local scale hierarchical salient feature fusion network is used to fuse the features of aligned video frames to extract contextual information and locally salient features to improve the reconstruction quality of the video. Compared with existing video super-resolution methods, this method achieves good performance and clearer edge and texture details.

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