Simultaneous DFT and IDFT through Widely Linear CLMS

Zhang, Xing; Dees, Bruno Scalzo; Li, Chunguo; Xia, Yili; Yang, Lüxi; Mandic, Danilo P.

doi:10.1109/icassp.2019.8683135

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…ey complete the task by maximizing the confidence of the part position in the image. Specifically, AAMs and their follow-ups [24][25][26] attempt to jointly model global holistic appearance and shape, while CLMs and variants [27,28] instead learn a group of local experts via imposing various shape constraints. In the framework of Cascaded Regression, the main operation is that vector addition is efficient and the computational complexity is low.…”

Section: Related Workmentioning

confidence: 99%

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Wei

Li²,

Chen³

et al. 2021

Mathematical Problems in Engineering

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Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To address this problem, we properly propose Face Restoration Generative Adversarial Networks to improve the resolution and restore the details of the blurred face. They include the Head Pose Estimation Network, Postural Transformer Network, and Face Generative Adversarial Networks. In this paper, we employ the following: (i) Swish-B activation function that is used in Face Generative Adversarial Networks to accelerate the convergence speed of the cross-entropy cost function, (ii) a special prejudgment monitor that is added to improve the accuracy of the discriminant, and (iii) the modified Postural Transformer Network that is used with 3D face reconstruction network to correct faces at different expression pose angles. Our method improves the resolution of face image and performs well in image restoration. We demonstrate how our method can produce high-quality faces, and it is superior to the most advanced methods on the reconstruction task of blind faces for in-the-wild images; especially, our 8 × SR SSIM and PSNR are, respectively, 0.078 and 1.16 higher than FSRNet in AFLW.

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