# Guiding WaveMamba with Frequency Maps for Image Debanding

arXiv: https://arxiv.org/abs/2508.11331 · HTML: https://mlsanity.com/paper/2508.11331/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Wave-Mamba: Wavelet State Space Model for Ultra-High-Definition Low-Light Image Enhancement](https://mlsanity.com/paper/2408.01276/) (2024)
- [A Technical Overview of AV1](https://mlsanity.com/paper/2008.06091/) (2020)
- [BBAND Index: A No-Reference Banding Artifact Predictor](https://mlsanity.com/paper/2002.11891/) (2020)
- [CAMBI: Contrast-aware Multiscale Banding Index](https://mlsanity.com/paper/2102.00079/) (2021)
- [Adaptive Debanding Filter](https://mlsanity.com/paper/2009.10804/) (2020)
- [BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality Assessment](https://mlsanity.com/paper/2311.17752/) (2023)
- [Deep Image Debanding](https://mlsanity.com/paper/2110.08569/) (2021)

## Abstract (arXiv)

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.