Neural Network Inpainting for Multi-Object Removal
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Solution Overview
Problem
Current image editing technologies require significant manual effort to remove objects from digital images while maintaining visual appeal, and existing automated methods are limited in their ability to perform multi-object inpainting without manual masking or selection.
Innovation Solution
A deep learning-based system using variational autoencoders (VAEs) and generative adversarial networks (GANs) for unsupervised or semi-supervised multi-element image and video inpainting, which learns object features from reference images to automatically mask and remove objects from source images, filling gaps with contextually appropriate content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated object removal methods are used, then productivity is improved, but manufacturing precision deteriorates due to inability to perform multi-object inpainting
Solution Approach 1:
The system segments the object removal task by training separate encoder networks for different object classes (e.g., person, vehicle, animal). Each encoder specializes in detecting and removing specific types of objects, enabling multi-object inpainting while maintaining high accuracy for each object type through dedicated processing pathways.
Solution Approach 2:
The system creates a universal inpainting framework that can handle multiple object classes through a single GAN architecture. The generator and discriminator networks work together to fill various types of object removal gaps (people, vehicles, animals, etc.) using learned features from reference images, achieving both high productivity and precision across diverse object types.
2Manufacturing precision
If manual masking is required, then manufacturing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service by automatically detecting objects to be removed and generating masks without user intervention. The encoder networks automatically identify object boundaries and create segmentation masks, while the GAN system automatically fills the removed regions with contextually appropriate content, eliminating the need for manual masking while maintaining high inpainting accuracy.
Solution Approach 2:
The system introduces an intermediary automatic object detection and masking system between the user input and the inpainting process. This intermediary layer uses trained encoders to automatically generate accurate masks for multiple object types, bridging the gap between simple user input and high-quality inpainting results without requiring manual mask creation.
3Adaptability or versatility
If reference images are used for feature learning, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary action by pre-training encoder networks on reference images containing various object classes before actual object removal tasks. The encoders learn robust object features, boundaries, and characteristics from these reference images during a training phase, enabling the system to automatically recognize and remove diverse object types without increasing operational complexity during actual use.
Solution Approach 2:
The system uses copying by creating multiple encoder networks that replicate the same architectural structure but are specialized for different object classes. Each encoder copies the fundamental feature extraction capabilities while adapting to specific object types through training on reference images, enabling versatile object recognition without requiring a completely different architecture for each object class.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to remove objects from images and perform inpainting for regions of object removal. In at least one embodiment, one or more neural networks are used to remove one or more objects from one or more images, wherein the one or more objects are of a similar type.