Face Image Restoration via Structured Graph Convolution
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Solution Overview
Problem
Existing face image restoration methods struggle to accurately capture and learn structured information due to limitations in traditional convolution operations, leading to poor accuracy and visualization effects in face restoration tasks.
Innovation Solution
A face image restoration method utilizing a pre-trained restoration model that includes an encoder, a structured face graph generation module, an adaptive adjacency matrix generator, a face graph convolution network, and a decoder, which generates a structured face graph and uses graph convolution to restore face images effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional convolution operations are used for face image restoration, then the device complexity is low, but the accuracy of capturing structured information is poor
Solution Approach 1:
The patent segments the face image into multiple feature blocks and constructs a face graph where each block becomes a node. This segmentation allows the model to capture structured information more effectively by representing spatial relationships explicitly in the graph structure, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The patent introduces a graph convolution network as an intermediary between the encoder and decoder. The graph convolution operates on the constructed face graph to capture structured information that traditional convolutions miss, thereby improving accuracy while maintaining manageable model complexity through the mediating graph structure.
2Reliability
If traditional convolution networks are used, then the model structure is simple, but the ability to learn structured information is insufficient
Solution Approach 1:
The patent transitions from traditional 2D convolution operations to graph-based operations, adding a new dimension of structural representation. By mapping face blocks to graph nodes and using graph convolutions, the model captures spatial relationships and structured information more effectively, improving reliability without excessive complexity increase.
Solution Approach 2:
The restoration model combines multiple components: encoder, face graph construction module, graph convolution network, and decoder. This composite structure integrates traditional convolution with graph-based structured processing, achieving high reliability through the synergistic combination of different processing mechanisms.
3Measurement precision
If graph convolution network is introduced, then the accuracy of structured information learning is improved, but the computational complexity increases
Solution Approach 1:
By segmenting the face image into discrete blocks and representing them as graph nodes, the computational complexity is managed through structured representation. The graph convolution operates on this segmented structure, improving accuracy while keeping computational requirements manageable through the organized node-block architecture.
Solution Approach 2:
The graph convolution network applies different processing to different regions of the face graph, capturing local structured information with high precision. This local quality approach improves overall accuracy by treating different face blocks with appropriate graph-based operations while managing computational load through localized processing.
Data Source
AI summary
The present application discloses a face image restoration method, a system, a storage medium and a device, the restoration model adopted in the above method starts from structured information of a face, generates a structured face graph based on features of the to-be-restored face image, and restores face image by the structured face graph and the decoder, which can solve the problem that it is difficult to capture and learn structured information based on the traditional convolution operation, and improve the indicators of the face restoration and enriches the visualization effect.