Decontamination Transformer For Blind Image Inpainting

Blind image inpainting aims at recovering the content from a corrupted image in which the mask indicating the corrupted regions is not available in inference time. Inspired that most existing methods for inpainting suffer from complex contamination, we propose a model that explicitly predicts the re...

Full description

Saved in:
Bibliographic Details
Main Authors: Li, Chun-Yi, Lin, Yen-Yu, Chiu, Wei-Chen
Format: Conference Proceeding
Language:English
Subjects:
Acoustics
Blind image inpainting
Decontamination
Interference
Predictive models
Semantics
Signal processing
Transformer
Transformers
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Blind image inpainting aims at recovering the content from a corrupted image in which the mask indicating the corrupted regions is not available in inference time. Inspired that most existing methods for inpainting suffer from complex contamination, we propose a model that explicitly predicts the realvalued alpha mask and contaminant to eliminate the contamination from the corrupted image, thus improving the inpainting performance. To enhance the overall semantic consistency, the attention mechanism of transformers is exploited and integrated into our inpainting network. We conduct extensive experiments to verify our method against blind and non-blind inpainting models and demonstrate its effectiveness and generalizability to different sources of contaminant.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10094950