Deep Face Super-Resolution with Iterative Collaboration between
Attentive Recovery and Landmark Estimation
- URL: http://arxiv.org/abs/2003.13063v1
- Date: Sun, 29 Mar 2020 16:04:48 GMT
- Title: Deep Face Super-Resolution with Iterative Collaboration between
Attentive Recovery and Landmark Estimation
- Authors: Cheng Ma, Zhenyu Jiang, Yongming Rao, Jiwen Lu, Jie Zhou
- Abstract summary: We propose a deep face super-resolution (FSR) method with iterative collaboration between two recurrent networks.
In each recurrent step, the recovery branch utilizes the prior knowledge of landmarks to yield higher-quality images.
A new attentive fusion module is designed to strengthen the guidance of landmark maps.
- Score: 92.86123832948809
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent works based on deep learning and facial priors have succeeded in
super-resolving severely degraded facial images. However, the prior knowledge
is not fully exploited in existing methods, since facial priors such as
landmark and component maps are always estimated by low-resolution or coarsely
super-resolved images, which may be inaccurate and thus affect the recovery
performance. In this paper, we propose a deep face super-resolution (FSR)
method with iterative collaboration between two recurrent networks which focus
on facial image recovery and landmark estimation respectively. In each
recurrent step, the recovery branch utilizes the prior knowledge of landmarks
to yield higher-quality images which facilitate more accurate landmark
estimation in turn. Therefore, the iterative information interaction between
two processes boosts the performance of each other progressively. Moreover, a
new attentive fusion module is designed to strengthen the guidance of landmark
maps, where facial components are generated individually and aggregated
attentively for better restoration. Quantitative and qualitative experimental
results show the proposed method significantly outperforms state-of-the-art FSR
methods in recovering high-quality face images.
Related papers
- Analysis and Benchmarking of Extending Blind Face Image Restoration to Videos [99.42805906884499]
We first introduce a Real-world Low-Quality Face Video benchmark (RFV-LQ) to evaluate leading image-based face restoration algorithms.
We then conduct a thorough systematical analysis of the benefits and challenges associated with extending blind face image restoration algorithms to degraded face videos.
Our analysis identifies several key issues, primarily categorized into two aspects: significant jitters in facial components and noise-shape flickering between frames.
arXiv Detail & Related papers (2024-10-15T17:53:25Z) - Prior Knowledge Distillation Network for Face Super-Resolution [25.188937155619886]
The purpose of face super-resolution (FSR) is to reconstruct high-resolution (HR) face images from low-resolution (LR) inputs.
We propose a prior knowledge distillation network (PKDN) for FSR, which involves transferring prior information from the teacher network to the student network.
arXiv Detail & Related papers (2024-09-22T09:58:20Z) - Analysis of Deep Image Prior and Exploiting Self-Guidance for Image
Reconstruction [13.277067849874756]
We study how DIP recovers information from undersampled imaging measurements.
We introduce a self-driven reconstruction process that concurrently optimize both the network weights and the input.
Our method incorporates a novel denoiser regularization term which enables robust and stable joint estimation of both the network input and reconstructed image.
arXiv Detail & Related papers (2024-02-06T15:52:23Z) - Cross-resolution Face Recognition via Identity-Preserving Network and
Knowledge Distillation [12.090322373964124]
Cross-resolution face recognition is a challenging problem for modern deep face recognition systems.
This paper proposes a new approach that enforces the network to focus on the discriminative information stored in the low-frequency components of a low-resolution image.
arXiv Detail & Related papers (2023-03-15T14:52:46Z) - A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur,
Artifact Removal [177.21001709272144]
Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images.
This paper comprehensively surveys recent advances in deep learning techniques for face restoration.
arXiv Detail & Related papers (2022-11-05T07:08:15Z) - Face Super-Resolution with Progressive Embedding of Multi-scale Face
Priors [4.649637261351803]
We propose a novel recurrent convolutional network based framework for face super-resolution.
We take full advantage of the intermediate outputs of the recurrent network, and landmarks information and facial action units (AUs) information are extracted.
Our proposed method significantly outperforms state-of-the-art FSR methods in terms of image quality and facial details restoration.
arXiv Detail & Related papers (2022-10-12T08:16:52Z) - Hierarchical Similarity Learning for Aliasing Suppression Image
Super-Resolution [64.15915577164894]
A hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing.
HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively.
arXiv Detail & Related papers (2022-06-07T14:55:32Z) - Implicit Subspace Prior Learning for Dual-Blind Face Restoration [66.67059961379923]
A novel implicit subspace prior learning (ISPL) framework is proposed as a generic solution to dual-blind face restoration.
Experimental results demonstrate significant perception-distortion improvement of ISPL against existing state-of-the-art methods.
arXiv Detail & Related papers (2020-10-12T08:04:24Z) - Joint Deep Learning of Facial Expression Synthesis and Recognition [97.19528464266824]
We propose a novel joint deep learning of facial expression synthesis and recognition method for effective FER.
The proposed method involves a two-stage learning procedure. Firstly, a facial expression synthesis generative adversarial network (FESGAN) is pre-trained to generate facial images with different facial expressions.
In order to alleviate the problem of data bias between the real images and the synthetic images, we propose an intra-class loss with a novel real data-guided back-propagation (RDBP) algorithm.
arXiv Detail & Related papers (2020-02-06T10:56:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.