Research on joint multi-scale convolution network for image super-resolution reconstruction

Ziwei Lu; Yan Zhang

doi:10.1117/12.2660518

3 February 2023 Research on joint multi-scale convolution network for image super-resolution reconstruction

Ziwei Lu, Yan Zhang

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125113G (2023) https://doi.org/10.1117/12.2660518
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

To solve the problem that the extracted features are not accurate due to the use of single-size convolution kernel in convolution neural network super-resolution reconstruction algorithm, a network structure combining multi-scale features is proposed. The structure consists of a multi-scale feature extraction block and a reconstruction module. Multiple convolution kernels are adopt to extract he multi-scale feature in multi-scale feature extraction module, and sub-pixel convolution layer is used to enlarge the feature image size to high-resolution image size in the image reconstruction module. The deep network model in this paper fully considers the importance of multi-scale features and can better reconstruct the high-frequency details of the image. The experimental results show that the improved network structure model can enhance the quality of image reconstruction and can better deal with the problem of image super-resolution reconstruction.

Citation Download Citation

Ziwei Lu and Yan Zhang "Research on joint multi-scale convolution network for image super-resolution reconstruction", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125113G (3 February 2023); https://doi.org/10.1117/12.2660518

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KEYWORDS

Convolution

Super resolution

Reconstruction algorithms

Feature extraction

Data modeling

Image quality

Neural networks

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