Text

Recursive Copy and Paste GAN : Face Hallucination From Shaded Thumbnails

Existing face hallucination methods based on convolutional neural networks (CNNs) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in non-uniform illumination conditions. This paper proposes a Recursive Copy and Paste Generative Adversarial Network (Re-CPGAN) to recover authentic high-resolution (HR) face images while compensating for non-uniform illumination. To this end, we develop two key components in our Re-CPGAN: internal and recursive external Copy and Paste networks (CPnets). Our internal CPnet exploits facial self-similarity information residing in the input image to enhance facial details; while our recursive external CPnet leverages an external guided face for illumination compensation. Specifically, our recursive external CPnet stacks multiple external Copy and Paste (EX-CP) units in a compact model to learn normal illumination and enhance facial details recursively. By doing so, our method offsets illumination and upsamples facial details progressively in a coarse-to-fine fashion, thus alleviating the ambiguity of correspondences between LR inputs and external guided inputs. Furthermore, a new illumination compensation loss is developed to capture illumination from the external guided face image effectively. Extensive experiments demonstrate that our method achieves authentic HR face images in a uniform illumination condition with a 16× magnification factor and outperforms state-of-the-art methods qualitatively and quantitatively.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.8 August 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 4321-4338

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

SUPER-RESOLUTION
GENERATIVE ADVERSARIAL NETWORK
FACE HALLUCINATION
ILLUMINATION NORMALIZATION

Info Detail Spesifik

DOI: 10.1109/TPAMI.2021.3061312

Pernyataan Tanggungjawab

Yang Zhang, Ivor W. Tsang, Yawei Luo, Changhui Hu, Xiaobo Lu, Xin Yu

Versi lain/terkait

Lampiran Berkas

Komentar