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.

翻译：当JPEG图像采用高压缩率的有损压缩方法进行压缩时，图像中可能出现块状效应，因此需要将图像恢复至原始质量。特别地，恢复不可识别的压缩图像是一项创新性挑战。为此，本文致力于采用基于生成对抗网络的方法，解决因最大压缩而遭受严重损失的JPEG图像恢复问题。该网络中的生成器基于U-Net架构，并采用新颖的沙漏结构，该结构能够保留深层特征。此外，网络结合了两种损失函数——低频损失和高频损失，用于生成自然且高性能的图像。高频损失使用预训练的VGG-16网络，并通过最能表征特征的特定层进行配置，从而提升高频区域的性能；低频损失则用于处理低频区域。这两种损失函数促使生成器生成能够欺骗判别器的图像，同时精准重建高频与低频区域。实验结果表明，该方法消除了有损压缩图像中的块状效应，并生成了可识别的人脸身份。本研究在图像恢复性能上较以往研究取得了显著提升。