Research on media image algorithms based on cross-scale fusion networks

Siqi Liu

doi:10.1117/12.2674959

2 May 2023 Research on media image algorithms based on cross-scale fusion networks

Siqi Liu

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126421V (2023) https://doi.org/10.1117/12.2674959
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

To address the problem of inaccurate estimation of model parameters by non-end-to-end image defogging algorithms based on deep learning, and the problem of image spatial information retention by current end-to-end image defogging algorithms based on deep learning, this paper proposes a single-image defogging model DeHRNet based on High-Resolution Net (HRNet). DeHRNet is divided into branches with different resolutions, and branches with different resolutions are connected in parallel and multi-scale fusion is performed at the end of each stage. This paper adds a new stage to the original network to make it better for image defogging work. The addition of a new stage to collect feature map representations of all branches of the network by up sampling to enhance the high resolutions representation, rather than using only feature maps of the high resolutions branches, makes the recovered fog-free images more natural and significant. The experimental results show that DeHRNet has a significant de-fogging effect on fogged images.

Citation Download Citation

Siqi Liu "Research on media image algorithms based on cross-scale fusion networks", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126421V (2 May 2023); https://doi.org/10.1117/12.2674959

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KEYWORDS

Convolution

Image fusion

Education and training

Deep learning

Feature extraction

Fiber optic gyroscopes

Matrices

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