Restoration of the JPEG Maximum Lossy Compressed Face Images with
  Hourglass Block based on Early Stopping Discriminator
Jongwook Si Sungyoung Kim
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.