# Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block based on Early Stopping Discriminator

arXiv: https://arxiv.org/abs/2306.12757 · HTML: https://mlsanity.com/paper/2306.12757/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [Deformable GANs for Pose-based Human Image Generation](https://mlsanity.com/paper/1801.00055/) (2017)
- [Learning Better Lossless Compression Using Lossy Compression](https://mlsanity.com/paper/2003.10184/) (2020)
- [On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework](https://mlsanity.com/paper/2106.02782/) (2021)

## Abstract (arXiv)

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.