# Blind Inpainting with Object-aware Discrimination for Artificial Marker Removal

arXiv: https://arxiv.org/abs/2303.15124 · HTML: https://mlsanity.com/paper/2303.15124/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://mlsanity.com/paper/2202.09778/) (2022)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model](https://mlsanity.com/paper/2212.00490/) (2022)
- [MAT: Mask-Aware Transformer for Large Hole Image Inpainting](https://mlsanity.com/paper/2203.15270/) (2022)
- [Image inpainting: A review](https://mlsanity.com/paper/1909.06399/) (2019)
- [Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding](https://mlsanity.com/paper/2203.00867/) (2022)
- [PD-GAN: Probabilistic Diverse GAN for Image Inpainting](https://mlsanity.com/paper/2105.02201/) (2021)
- [CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training](https://mlsanity.com/paper/2203.11947/) (2022)
- [Reduce Information Loss in Transformers for Pluralistic Image Inpainting](https://mlsanity.com/paper/2205.05076/) (2022)
- [DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models](https://mlsanity.com/paper/2302.06826/) (2023)
- [VCNet: A Robust Approach to Blind Image Inpainting](https://mlsanity.com/paper/2003.06816/) (2020)
- [Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report](https://mlsanity.com/paper/2210.12113/) (2022)
- [Deep Blind Image Inpainting](https://mlsanity.com/paper/1712.09078/) (2017)
- [Context Encoding Chest X-rays](https://mlsanity.com/paper/1812.00964/) (2018)

## Built on by (most cited first, 1 of 1)

- [Echo-DM: Ultrasound Marker Removal via Conditional Latent Diffusion and Region-Aware Fusion](https://mlsanity.com/paper/2606.09378/) (2026)

## Abstract (arXiv)

Medical images often incorporate doctor-added markers that can hinder AI-based diagnosis. This issue highlights the need of inpainting techniques to restore the corrupted visual contents. However, existing methods require manual mask annotation as input, limiting the application scenarios. In this paper, we propose a novel blind inpainting method that automatically reconstructs visual contents within the corrupted regions without mask input as guidance. Our model includes a blind reconstruction network and an object-aware discriminator for adversarial training. The reconstruction network contains two branches that predict corrupted regions in images and simultaneously restore the missing visual contents. Leveraging the potent recognition capability of a dense object detector, the object-aware discriminator ensures markers undetectable after inpainting. Thus, the restored images closely resemble the clean ones. We evaluate our method on three datasets of various medical imaging modalities, confirming better performance over other state-of-the-art methods.