用频率图引导的WaveMamba模型，有效修复低码率压缩带来的条带伪影。
Guiding WaveMamba with Frequency Maps for Image Debanding
- 结合小波状态空间模型与频率掩码图，精准恢复高频细节
- 在BAND-2k数据集上达到0.082的DBI值，优于当前最佳方法
- 开源工具链+公开基准，适合图像修复与视频编解码研究者
现代编码器在低码率下压缩常导致条带伪影，尤其在天空等平滑区域。这些伪影降低视觉质量，且因反复转码在用户生成内容中普遍。本文提出一种后处理修复方法，利用小波状态空间模型（Wavelet State Space Model）与频率掩码图，有效保留高频细节。同时构建了开源条带修复方法基准，评估其在两个公开数据集上的表现。实验表明，该方法在BAND-2k数据集上达到0.082的DBI值，显著优于现有最优方法，且保持纹理清晰。视觉效果验证了其有效性。代码与补充材料已公开于：https://github.com/xinyiW915/Debanding-PCS2025。
原文摘要 · Abstract (English)
Compression at low bitrates in modern codecs often introduces banding artifacts, especially in smooth regions such as skies. These artifacts degrade visual quality and are common in user-generated content due to repeated transcoding. We propose a banding restoration method that employs the Wavelet State Space Model and a frequency masking map to preserve high-frequency details. Furthermore, we provide a benchmark of open-source banding restoration methods and evaluate their performance on two public banding image datasets. Experimentation on the available datasets suggests that the proposed post-processing approach effectively suppresses banding compared to the state-of-the-art method (a DBI value of 0.082 on BAND-2k) while preserving image textures. Visual inspections of the results confirm this. Code and supplementary material are available at: https://github.com/xinyiW915/Debanding-PCS2025.
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