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Iterative Back Projection Network Based on Deformable 3D Convolution
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DOI：10.1109/ACCESS.2023.3325659.png)
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摘要
En 中文
Video super-resolution technology enhances the display quality of videos by obtaining high-resolution videos from low-resolution videos. Unlike single-image super-resolution, utilizing information between adjacent video frames is crucial in video super-resolution. To improve the performance of video super-resolution reconstruction, a model combining deformable 3D convolution and iterative back projection is proposed to fully exploit the temporal-spatial correlation of video frames. The model takes multiple consecutive video frames as input and outputs the super-resolution reconstruction of the middle frame, including three modules: multi-scale feature extraction, feature fusion, and high-resolution reconstruction. Firstly, multi-scale 3D convolution is used for preliminary feature extraction. Then, deformable 3D convolution and iterative back projection are combined for feature fusion. Finally, multiple residual dense blocks and sub-pixel convolution are used for high-resolution reconstruction, and global residual connections are utilized to obtain the reconstructed high-resolution video. Experimental results on the Vid4 dataset demonstrate that compared to existing methods, this method can effectively improve the peak signal-to-noise ratio and structural similarity performance and achieve better visual effects with 4x super-resolution magnification.
Keyword:
Superresolution
Image reconstruction
Feature extraction
Convolution
Three-dimensional displays
Image resolution
Optical flow
Video super-resolution
adjacent video frames
deformable 3D convolution
iterative back-projection
multi-scale feature extraction
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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