Abstract
The problem of restoring high-resolution images from blurry images has long been a concern, and traditional methods of directly interpolating low-resolution images to obtain high-resolution images are simple but ineffective. Inspired by SR3, we propose a super-resolution model of human faces based on the diffusion model, which achieves super-resolution through a random iterative denoising process. In this paper, we have used a residual block that integrates multi-scale spatial attention and coordinate attention. Additionally, we have enhanced the restoration of image details through a global attention model. These advancements effectively address the discrepancy between automated evaluation metrics and human perception in high-frequency details for super-resolution models. Through evaluation of the standard eight-fold super-resolution task on CelebA-HQ, our model performs well and achieves competitive scores on SSIM and PSNR metrics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114
Kingma DP, Dhariwal P (2018) Glow: generative flow with invertible \(1\times 1\) convolutions. In: Advances in neural information processing systems, vol 31
Karras T, Aila T, Laine S, Lehtinen J (2017) Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196
Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of wasserstein GANs. In: Advances in neural information processing systems, vol 30
Ravuri S, Vinyals O (2019) Classification accuracy score for conditional generative models. In: Advances in neural information processing systems, vol 32
Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 136–144
Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, Qiao Y, Loy CC (2018) ESRGAN: enhanced super-resolution generative adversarial networks. In: Proceedings of the European conference on computer vision (ECCV) workshops, p 0
Liang J, Cao J, Sun G, Zhang K, Van Gool L, Timofte R (2021) SwinIR: image restoration using Swin transformer. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 1833–1844
Chen Y, Tai Y, Liu X, Shen C, Yang J (2018) FSRNet: end-to-end learning face super-resolution with facial priors. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2492–2501
Menon S, Damian A, Hu S, Ravi N, Rudin C (2020) Pulse: self-supervised photo upsampling via latent space exploration of generative models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 2437–2445
Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B (2022) High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10684–10695
Ho J, Saharia C, Chan W, Fleet DJ, Norouzi M, Salimans T (2022) Cascaded diffusion models for high fidelity image generation. J Mach Learn Res 23(47):1–33
Saharia C, Ho J, Chan W, Salimans T, Fleet DJ, Norouzi M (2022) Image super-resolution via iterative refinement. IEEE Trans Pattern Anal Mach Intell
Su J-N, Gan M, Chen G-Y, Yin J-L, Chen CP (2022) Global learnable attention for single image super-resolution. IEEE Trans Pattern Anal Mach Intell
Gao S, Liu X, Zeng B, Xu S, Li Y, Luo X, Liu J, Zhen X, Zhang B (2023) Implicit diffusion models for continuous super-resolution. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10021–10030
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Feng, T., Xie, Y. (2024). Face Super-Resolution Model Based on Diffusion Model. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_6
Download citation
DOI: https://doi.org/10.1007/978-981-99-7502-0_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7555-6
Online ISBN: 978-981-99-7502-0
eBook Packages: EngineeringEngineering (R0)