# Deep Image Debanding

arXiv: https://arxiv.org/abs/2110.08569 · HTML: https://mlsanity.com/paper/2110.08569/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (2017)
- [Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal](https://mlsanity.com/paper/1503.00593/) (2015)
- [Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation](https://mlsanity.com/paper/2007.05946/) (2020)
- [dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs](https://mlsanity.com/paper/1904.06505/) (2019)
- [BBAND Index: A No-Reference Banding Artifact Predictor](https://mlsanity.com/paper/2002.11891/) (2020)
- [Perceptual Video Quality Prediction Emphasizing Chroma Distortions](https://mlsanity.com/paper/2009.11203/) (2020)
- [CAMBI: Contrast-aware Multiscale Banding Index](https://mlsanity.com/paper/2102.00079/) (2021)
- [Adaptive Debanding Filter](https://mlsanity.com/paper/2009.10804/) (2020)

## Built on by (most cited first, 2 of 2)

- [Subjective and Objective Quality Assessment of Banding Artifacts on Compressed Videos](https://mlsanity.com/paper/2508.08700/) (2025)
- [Guiding WaveMamba with Frequency Maps for Image Debanding](https://mlsanity.com/paper/2508.11331/) (2025)

## Abstract (arXiv)

Banding or false contour is an annoying visual artifact whose impact is even more pronounced in ultra high definition, high dynamic range, and wide colour gamut visual content, which is becoming increasingly popular. Since users associate a heightened expectation of quality with such content and banding leads to deteriorated visual quality-of-experience, the area of banding removal or debanding has taken paramount importance. Existing debanding approaches are mostly knowledge-driven. Despite the widespread success of deep learning in other areas of image processing and computer vision, data-driven debanding approaches remain surprisingly missing. In this work, we make one of the first attempts to develop a deep learning based banding artifact removal method for images and name it deep debanding network (deepDeband). For its training, we construct a large-scale dataset of 51,490 pairs of corresponding pristine and banded image patches. Performance evaluation shows that deepDeband is successful at greatly reducing banding artifacts in images, outperforming existing methods both quantitatively and visually.