OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal
Deep learning-based methods have shown remarkable performance in single JPEG artifacts removal task. However, existing methods tend to degrade on double JPEG images, which are prevalent in real-world scenarios. To address this issue, we propose Offset-Aware Partition Transformer for double JPEG arti...
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Main Authors | , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
21.08.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Deep learning-based methods have shown remarkable performance in single JPEG
artifacts removal task. However, existing methods tend to degrade on double
JPEG images, which are prevalent in real-world scenarios. To address this
issue, we propose Offset-Aware Partition Transformer for double JPEG artifacts
removal, termed as OAPT. We conduct an analysis of double JPEG compression that
results in up to four patterns within each 8x8 block and design our model to
cluster the similar patterns to remedy the difficulty of restoration. Our OAPT
consists of two components: compression offset predictor and image
reconstructor. Specifically, the predictor estimates pixel offsets between the
first and second compression, which are then utilized to divide different
patterns. The reconstructor is mainly based on several Hybrid Partition
Attention Blocks (HPAB), combining vanilla window-based self-attention and
sparse attention for clustered pattern features. Extensive experiments
demonstrate that OAPT outperforms the state-of-the-art method by more than
0.16dB in double JPEG image restoration task. Moreover, without increasing any
computation cost, the pattern clustering module in HPAB can serve as a plugin
to enhance other transformer-based image restoration methods. The code will be
available at https://github.com/QMoQ/OAPT.git . |
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DOI: | 10.48550/arxiv.2408.11480 |