Multi-scale progressive feedback based on a self-attentive mechanism image super-resolution reconstruction

Yao Wang; Ronggui Wang; Zhiyi Huang

doi:10.1117/12.2684964

19 October 2023 Multi-scale progressive feedback based on a self-attentive mechanism image super-resolution reconstruction

Yao Wang, Ronggui Wang, Zhiyi Huang

Author Affiliations +

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127092R (2023) https://doi.org/10.1117/12.2684964
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Convolutional neural networks have shown amazing recovery in image super-resolution in recent years. Despite the great success of CNN-based methods, deploying these model methods to devices with low computational power is difficult due to the large number of parameters to which the super-resolution methods of recent years have been applied. To address this problem, this paper proposes a multi-scale progressive feedback pixel attention network. Specifically, a multi-scale progressive feedback attention block is designed to complete the attention structure using a multi-scale feedback module, which references the architectural approach of the self-attentive mechanism in the design of the multi-scale module by removing its secondary complexity and adopting The multi-scale module is designed to accept all vectors as input and to learn in an incremental manner. Depending on the depth of the network, the MPFAB is able to retain information at different depths and can adequately fuse information from networks of different depths to mirror each other. Experiments show that the proposed MPFAN has better reconstruction results than existing methods with a smaller number of parameters.

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Yao Wang, Ronggui Wang, and Zhiyi Huang "Multi-scale progressive feedback based on a self-attentive mechanism image super-resolution reconstruction", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127092R (19 October 2023); https://doi.org/10.1117/12.2684964

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KEYWORDS

Super resolution

Education and training

Image restoration

Convolution

Image quality

Design and modelling

Feature extraction

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