Structure–Texture Guided Image Inpainting for Large Damaged Areas
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Solution Overview
Problem
Neural network models struggle with restoring large damaged areas in images, resulting in distorted structures and blurred textures, leading to a decline in inpainting quality.
Innovation Solution
An image inpainting method and system that integrates a generator and discriminator with a structure and texture interaction module, gated interaction unit, and multi-view local reconstruction network, utilizing a three-encoder network architecture to synchronize structure-oriented texture synthesis and texture-guided structure restoration, enhancing feature extraction and restoration quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If neural network models are used to restore large damaged areas in images, then the restoration coverage is improved, but the structural accuracy and texture quality deteriorate
Solution Approach 1:
The patent divides the image restoration task into multiple sub-tasks by segmenting the damaged area into different regions. The multi-view local reconstruction network processes different views and regions of the damaged area separately, then combines the results. This segmentation allows each sub-region to be restored with high precision while maintaining overall coverage.
Solution Approach 2:
The patent introduces a multi-view perspective to the restoration process. Instead of restoring the damaged area from a single view, the system generates multiple views of the damaged region and processes them through different encoder branches (structure encoder and texture encoder). This dimensional expansion from single-view to multi-view restoration enables simultaneous improvement of structural accuracy and texture quality across large areas.
2Device complexity
If structure and texture information are processed separately, then the processing complexity is reduced, but the interaction between structure and texture is insufficient
Solution Approach 1:
The patent merges the structure encoder and texture encoder into a unified multi-view local reconstruction network. The structure information encoder and texture information encoder process different aspects of the same input simultaneously and interact through shared convolutional layers and gating mechanisms. This merging allows the system to maintain separate processing paths for structure and texture while ensuring their consistent integration in the restoration result.
Solution Approach 2:
The patent introduces an intermediate encoder that acts as a mediator between the structure information encoder and texture information encoder. The intermediate encoder processes combined structure-texture features and passes them to the decoder. This intermediary component facilitates smooth interaction and coordination between structure and texture processing branches, ensuring consistent integration without significantly increasing overall complexity.
Data Source
AI summary
Provided is an image inpainting method and system guided by an image structure and texture information. The method includes: constructing an image inpainting model with an interaction ability between the image structure and the texture information; preprocessing a damaged image sample to obtain a mask image sample and an edge structure image sample; inputting the damaged image sample, the mask image sample, and the edge structure image sample into the image inpainting model, training the image inpainting model, and generating weight information of the image inpainting model, to obtain a target image inpainting model; obtaining a damaged image to be restored, preprocessing the damaged image to be restored, to obtain a mask image and an edge structure image, separately inputting the damaged image to be restored, the mask image, and the edge structure image into the target image inpainting model, outputting, by the target image inpainting model, a restored image.