Progressive Semantic Face Deblurring

Lee, Tae Bok; Jung, Soo Hyun; Heo, Yong Seok

doi:10.1109/access.2020.3033890

Cited by 8 publications

(23 citation statements)

References 61 publications

(124 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, the other five deblurring methods of comparison are a Laplacian sharpening based algorithm [22], a well-known blind deconvolution technique [47], and three stateof-the-art models: DeblurGAN [42], Progressive Semantic Deblurring [48], and UMSN Face Deblurring [49]. DeblurGAN is a recent GAN-based model that is very successful for deblurring of general images, while Progressive Semantic Deblurring and UMSN are SOTA algorithms designed especiallyfor face deblurring .…”

Section: Comparison Of Different Deblurring Methodsmentioning

confidence: 99%

“…However, it also introduces artifacts (ringing effects) around the eye lids and the image margins and white speckles on the face. To acknowledge the state-of-the-art techniques in image enhancement, we compare the performance of our proposed approach with the performance of DeblurGAN [42], Progressive Semantic Deblurring [48], and UMSN Deblurring [49], all of which represent the recent advances in image deblurring. As shown in Figure 5d, DeblurGAN successfully sharpens the blurry face but is unable to recover most of the useful facial details important for face recognition.…”

Section: Svd-inspired Deblurring Networkmentioning

confidence: 99%

See 1 more Smart Citation

Balancing Heterogeneous Image Quality for Improved Cross-Spectral Face Recognition

Cao

Cen

Zhao

et al. 2021

Sensors

View full text Add to dashboard Cite

Matching infrared (IR) facial probes against a gallery of visible light faces remains a challenge, especially when combined with cross-distance due to deteriorated quality of the IR data. In this paper, we study the scenario where visible light faces are acquired at a short standoff, while IR faces are long-range data. To address the issue of quality imbalance between the heterogeneous imagery, we propose to compensate it by upgrading the lower-quality IR faces. Specifically, this is realized through cascaded face enhancement that combines an existing denoising algorithm (BM3D) with a new deep-learning-based deblurring model we propose (named SVDFace). Different IR bands, short-wave infrared (SWIR) and near-infrared (NIR), as well as different standoffs, are involved in the experiments. Results show that, in all cases, our proposed approach for quality balancing yields improved recognition performance, which is especially effective when involving SWIR images at a longer standoff. Our approach outperforms another easy and straightforward downgrading approach. The cascaded face enhancement structure is also shown to be beneficial and necessary. Finally, inspired by the singular value decomposition (SVD) theory, the proposed deblurring model of SVDFace is succinct, efficient and interpretable in structure. It is proven to be advantageous over traditional deblurring algorithms as well as state-of-the-art deep-learning-based deblurring algorithms.

show abstract

Section: Comparison Of Different Deblurring Methodsmentioning

confidence: 99%

Section: Svd-inspired Deblurring Networkmentioning

confidence: 99%

Balancing Heterogeneous Image Quality for Improved Cross-Spectral Face Recognition

Cao

Cen

Zhao

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The associate editor coordinating the review of this manuscript and approving it for publication was Taous Meriem Laleg-Kirati . maps [16], [17], [18], and deep features [19]. Despite these efforts, these methods [13], [15], [17] often suffer from over-smoothed and perceptually unnatural results.…”

Section: Introductionmentioning

confidence: 99%

“…Despite these efforts, these methods [13], [15], [17] often suffer from over-smoothed and perceptually unnatural results. Some SFID methods [14], [16], [18], [19] are effective at improving the perceptual qualities of deblurred images on the strength of generative adversarial networks (GANs) [20]. GANs have demonstrated an ability to generate realistic samples via a min-max game between a generator and a discriminator.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Semantic-Aware Face Deblurring With Pixel-Wise Projection Discriminator

2023

Self Cite

View full text Add to dashboard Cite

Most recent face deblurring methods have leveraged the distribution modeling ability of generative adversarial networks (GANs) to impose a constraint that the deblurred image should follow the distribution of sharp ground-truth images. However, generating sharp face images with high fidelity and realistic properties from a blurry face image remains challenging under the GAN framework. To this end, we focus on modeling the joint distribution of sharp face images and segmentation label maps for face image deblurring in a GAN framework. We propose a semantic-aware pixel-wise projection (SAPP) discriminator that models pixel-label matching with semantic label map information and generates a pixel-wise probability map of realness for the input image as well as a per-image probability. Moreover, we introduce a predictionweighted (PW) loss to focus on erroneous pixels in the output of the decoder, using per-pixel real/fake probability map to re-weight the contribution of each pixel in the decoder. Furthermore, we present a coarseto-fine training technique for the generator, which encourages the generator to focus on global consistency in the early training stages and local details in the later stages. Extensive experimental results show that our method outperforms existing methods both quantitatively and qualitatively in terms of perceptual image quality.INDEX TERMS Face image deblurring, semantic-aware pixel-wise projection discriminator, predictionweighted loss.

show abstract

A survey on facial image deblurring

Wang,

Xu,

Zheng

2023

Comp. Visual Media

View full text Add to dashboard Cite

When a facial image is blurred, it significantly affects high-level vision tasks such as face recognition. The purpose of facial image deblurring is to recover a clear image from a blurry input image, which can improve the recognition accuracy, etc. However, general deblurring methods do not perform well on facial images. Therefore, some face deblurring methods have been proposed to improve performance by adding semantic or structural information as specific priors according to the characteristics of the facial images. In this paper, we survey and summarize recently published methods for facial image deblurring, most of which are based on deep learning. First, we provide a brief introduction to the modeling of image blurring. Next, we summarize face deblurring methods into two categories: model-based methods and deep learning-based methods. Furthermore, we summarize the datasets, loss functions, and performance evaluation metrics commonly used in the neural network training process. We show the performance of classical methods on these datasets and metrics and provide a brief discussion on the differences between model-based and learning-based methods. Finally, we discuss the current challenges and possible future research directions.

show abstract

Progressive Semantic Face Deblurring

Cited by 8 publications

References 61 publications

Balancing Heterogeneous Image Quality for Improved Cross-Spectral Face Recognition

Balancing Heterogeneous Image Quality for Improved Cross-Spectral Face Recognition

Semantic-Aware Face Deblurring With Pixel-Wise Projection Discriminator

A survey on facial image deblurring

Contact Info

Product

Resources

About