# WaveFill: A Wavelet-based Generation Network for Image Inpainting

arXiv: https://arxiv.org/abs/2107.11027 · HTML: https://mlsanity.com/paper/2107.11027/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 50 references that are in the map.
- First posted on arXiv 23 Jul 2021.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis](https://mlsanity.com/paper/1612.07919/) (2016)
- [EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning](https://mlsanity.com/paper/1901.00212/) (2019)
- [StructureFlow: Image Inpainting via Structure-aware Appearance Flow](https://mlsanity.com/paper/1908.03852/) (2019)
- [Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations](https://mlsanity.com/paper/2007.06929/) (2020)
- [Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting](https://mlsanity.com/paper/1904.07475/) (2019)
- [Image Inpainting via Generative Multi-column Convolutional Neural Networks](https://mlsanity.com/paper/1810.08771/) (2018)
- [Diverse Image Inpainting with Bidirectional and Autoregressive Transformers](https://mlsanity.com/paper/2104.12335/) (2021)
- [Spatial Fusion GAN for Image Synthesis](https://mlsanity.com/paper/1812.05840/) (2018)
- [Wavelet-Based Dual-Branch Network for Image Demoireing](https://mlsanity.com/paper/2007.07173/) (2020)
- [Contextual-based Image Inpainting: Infer, Match, and Translate](https://mlsanity.com/paper/1711.08590/) (2017)
- [Cascade EF-GAN: Progressive Facial Expression Editing with Local Focuses](https://mlsanity.com/paper/2003.05905/) (2020)
- [Unbalanced Feature Transport for Exemplar-based Image Translation](https://mlsanity.com/paper/2106.10482/) (2021)
- [Attribute-aware Face Aging with Wavelet-based Generative Adversarial Networks](https://mlsanity.com/paper/1809.06647/) (2018)
- [Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-Resolution](https://mlsanity.com/paper/1910.04074/) (2019)
- [Positional Normalization](https://mlsanity.com/paper/1907.04312/) (2019)

## Built on by (most cited first, 30 of 44)

- [Wavelet Diffusion Models are fast and scalable Image Generators](https://mlsanity.com/paper/2211.16152/) (2022)
- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [SmartEraser: Remove Anything from Images using Masked-Region Guidance](https://mlsanity.com/paper/2501.08279/) (2025)
- [Modulated Contrast for Versatile Image Synthesis](https://mlsanity.com/paper/2203.09333/) (2022)
- [WaveGAN: Frequency-aware GAN for High-Fidelity Few-shot Image Generation](https://mlsanity.com/paper/2207.07288/) (2022)
- [LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models](https://mlsanity.com/paper/2405.14477/) (2024)
- [Marginal Contrastive Correspondence for Guided Image Generation](https://mlsanity.com/paper/2204.00442/) (2022)
- [Learning Prior Feature and Attention Enhanced Image Inpainting](https://mlsanity.com/paper/2208.01837/) (2022)
- [FreGAN: Exploiting Frequency Components for Training GANs under Limited Data](https://mlsanity.com/paper/2210.05461/) (2022)
- [GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2110.01254/) (2021)
- [Painterly Image Harmonization in Dual Domains](https://mlsanity.com/paper/2212.08846/) (2022)
- [HINT: High-quality INPainting Transformer with Mask-Aware Encoding and Enhanced Attention](https://mlsanity.com/paper/2402.14185/) (2024)
- [SpectralAR: Spectral Autoregressive Visual Generation](https://mlsanity.com/paper/2506.10962/) (2025)
- [Wavelets Are All You Need for Autoregressive Image Generation](https://mlsanity.com/paper/2406.19997/) (2024)
- [GeoRemover: Removing Objects and Their Causal Visual Artifacts](https://mlsanity.com/paper/2509.18538/) (2025)
- [Semi-Supervised Video Inpainting with Cycle Consistency Constraints](https://mlsanity.com/paper/2208.06807/) (2022)
- [Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt](https://mlsanity.com/paper/2305.04430/) (2023)
- [WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks](https://mlsanity.com/paper/2310.11595/) (2023)
- [Decoupling High and Low Frequencies for Faithful Image Generation with Fine Details](https://mlsanity.com/paper/2509.05441/) (2025)
- [Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning](https://mlsanity.com/paper/2303.13133/) (2023)
- [Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid](https://mlsanity.com/paper/2112.04107/) (2021)
- [Towards Interactive Image Inpainting via Sketch Refinement](https://mlsanity.com/paper/2306.00407/) (2023)
- [Learning to Scale Temperature in Masked Self-Attention for Image Inpainting](https://mlsanity.com/paper/2302.06130/) (2023)
- [SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting](https://mlsanity.com/paper/2307.14489/) (2023)
- [PASTA: Towards Flexible and Efficient HDR Imaging Via Progressively Aggregated Spatio-Temporal Alignment](https://mlsanity.com/paper/2403.10376/) (2024)
- [WavePaint: Resource-efficient Token-mixer for Self-supervised Inpainting](https://mlsanity.com/paper/2307.00407/) (2023)
- [High-Frequency Enhanced Hybrid Neural Representation for Video Compression](https://mlsanity.com/paper/2411.06685/) (2024)
- [All-in-One Image Restoration via Causal-Deconfounding Wavelet-Disentangled Prompt Network](https://mlsanity.com/paper/2603.03839/) (2026)
- [Inpainting-Driven Mask Optimization for Object Removal](https://mlsanity.com/paper/2403.15849/) (2024)
- [MxT: Mamba x Transformer for Image Inpainting](https://mlsanity.com/paper/2407.16126/) (2024)

## Abstract (arXiv)

Image inpainting aims to complete the missing or corrupted regions of images with realistic contents. The prevalent approaches adopt a hybrid objective of reconstruction and perceptual quality by using generative adversarial networks. However, the reconstruction loss and adversarial loss focus on synthesizing contents of different frequencies and simply applying them together often leads to inter-frequency conflicts and compromised inpainting. This paper presents WaveFill, a wavelet-based inpainting network that decomposes images into multiple frequency bands and fills the missing regions in each frequency band separately and explicitly. WaveFill decomposes images by using discrete wavelet transform (DWT) that preserves spatial information naturally. It applies L1 reconstruction loss to the decomposed low-frequency bands and adversarial loss to high-frequency bands, hence effectively mitigate inter-frequency conflicts while completing images in spatial domain. To address the inpainting inconsistency in different frequency bands and fuse features with distinct statistics, we design a novel normalization scheme that aligns and fuses the multi-frequency features effectively. Extensive experiments over multiple datasets show that WaveFill achieves superior image inpainting qualitatively and quantitatively.