Paper
19 July 2024 Large model-guided multimodal face image super-resolution
Zhihao Jiang, Yuezhong Chu, Heng Liu
Author Affiliations +
Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132130J (2024) https://doi.org/10.1117/12.3035384
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China
Abstract
Aiming at the issue of single-image super-resolution methods relying solely on the input images of poor resolution, which struggle to reconstruct satisfactory results when the details in the low-resolution images are insufficient, this paper proposes a text-guided images of low quality. The shortcomings of inadequate information in images with low resolution are compensated for by introducing text information as prior knowledge into the network. Specifically, a multi-modal network structure is proposed, which utilizes a text-image cross-modal pre-trained large model to extract shallow image feature information of different modalities. After deep feature extraction, sub-pixel convolution is employed for magnifying the images and reconstruct high-quality images. Experimental results indicate that the proposed method can utilize text information to reconstruct abundant details missed in low-resolution image scenarios. NIQE and PI metrics confirm the superior super-resolution performance of the approach.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Zhihao Jiang, Yuezhong Chu, and Heng Liu "Large model-guided multimodal face image super-resolution", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132130J (19 July 2024); https://doi.org/10.1117/12.3035384
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Super resolution

Image quality

Feature extraction

Image restoration

Image fusion

Transformers

Education and training

RELATED CONTENT


Back to Top