Research on super-resolution reconstruction of single frame image oriented to multi-type semantics

Kexin Liu

doi:10.1117/12.2600173

1 June 2021 Research on super-resolution reconstruction of single frame image oriented to multi-type semantics

Kexin Liu

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 1184803 (2021) https://doi.org/10.1117/12.2600173
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

Images with low resolution and fuzzy semantics will have a negative impact on subsequent tasks such as object detection and behavior prediction. Therefore, it is particularly important to improve the resolution of the image. Compared with the existing three types of resolution reconstruction methods, the convolutional neural network super-resolution technology (SRCNN) that emerged in 2014 has greatly improved the restoration accuracy. However, the model still has the problem of poor convergence performance when processing multi-type semantic image reconstruction. Aiming at network optimization, this paper proposes an IPSO-SRCNN model, which initialize the network weights by using the Improved Particle Swarm Optimization (IPSO) and modify the weights by combining the gradient descent (GD) method, so that the IPSO's global search capability and the GD's local search ability can be fused. This paper designs three experimental modules and compares with five reconstruction methods to verify the reliability and practicability of the proposed model on the one hand. On the other hand, it highlights the potential of the proposed model for the reconstruction of multi-scene semantics.

Citation Download Citation

Kexin Liu "Research on super-resolution reconstruction of single frame image oriented to multi-type semantics", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 1184803 (1 June 2021); https://doi.org/10.1117/12.2600173

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