mlsanityData to 14 Oct 2026

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

arXiv 2306.12757 ↗First posted 22 Jun 2023eess.IV cs.CVcited by 0

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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.

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.

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References · 16

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4 more
  1. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
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