4 research outputs found
PP-GAN : Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN
The objective of a style transfer is to maintain the content of an image
while transferring the style of another image. However, conventional research
on style transfer has a significant limitation in preserving facial landmarks,
such as the eyes, nose, and mouth, which are crucial for maintaining the
identity of the image. In Korean portraits, the majority of individuals wear
"Gat", a type of headdress exclusively worn by men. Owing to its distinct
characteristics from the hair in ID photos, transferring the "Gat" is
challenging. To address this issue, this study proposes a deep learning network
that can perform style transfer, including the "Gat", while preserving the
identity of the face. Unlike existing style transfer approaches, the proposed
method aims to preserve texture, costume, and the "Gat" on the style image. The
Generative Adversarial Network forms the backbone of the proposed network. The
color, texture, and intensity were extracted differently based on the
characteristics of each block and layer of the pre-trained VGG-16, and only the
necessary elements during training were preserved using a facial landmark mask.
The head area was presented using the eyebrow area to transfer the "Gat".
Furthermore, the identity of the face was retained, and style correlation was
considered based on the Gram matrix. The proposed approach demonstrated
superior transfer and preservation performance compared to previous studies
Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block based on Early Stopping Discriminator
When a JPEG image is compressed using the loss compression method with a high
compression rate, a blocking phenomenon can occur in the image, making it
necessary to restore the image to its original quality. In particular,
restoring compressed images that are unrecognizable presents an innovative
challenge. Therefore, this paper aims to address the restoration of JPEG images
that have suffered significant loss due to maximum compression using a
GAN-based net-work method. The generator in this network is based on the U-Net
architecture and features a newly presented hourglass structure that can
preserve the charac-teristics of deep layers. Additionally, the network
incorporates two loss functions, LF Loss and HF Loss, to generate natural and
high-performance images. HF Loss uses a pretrained VGG-16 network and is
configured using a specific layer that best represents features, which can
enhance performance for the high-frequency region. LF Loss, on the other hand,
is used to handle the low-frequency region. These two loss functions facilitate
the generation of images by the generator that can deceive the discriminator
while accurately generating both high and low-frequency regions. The results
show that the blocking phe-nomenon in lost compressed images was removed, and
recognizable identities were generated. This study represents a significant
improvement over previous research in terms of image restoration performance
A Novel Solution-Stamping Process for Preparation of a Highly Conductive Aluminum Thin Film
A novel solution-stamping process for the preparation of a highly conductive aluminum thin film on both rigid and flexible substrates is proposed. The superior electrical properties of Al thin films fabricated by the solution-stamping process compared to silver and gold films fabricated from colloidal nanoparticles are experimentally demonstrated, and their applications in electronic circuits on rigid and flexible substrates and to organic light-emitting diodes (OLEDs) are investigated.1134sciescopu