Multi-Guided Patch Match Inpainting for High-Resolution Image Texture Accuracy
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Solution Overview
Problem
Conventional digital image editing systems fail to flexibly select appropriate example pixels, leading to implausible results and inaccuracies in high-resolution image inpainting, particularly in capturing realistic textures and semantic layouts.
Innovation Solution
A hybrid pipeline of deep networks and patch-based synthesis is employed, utilizing multiple image guides to generate candidate inpainting results and a curation module for selecting the optimal inpainted image based on subtle comparisons, ensuring accurate and flexible inpainting at high resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional patch-based approaches are used to borrow example pixels from other portions of a digital image, then the inpainting process can be performed, but the system fails to flexibly select appropriate example pixels, leading to implausible results
Solution Approach 1:
The system dynamically selects and weights multiple image guides (depth map, normal map, semantic map, edge map) based on the specific characteristics of the inpainting region and surrounding context. This dynamic adaptation allows the system to flexibly choose the most appropriate example pixels for each local region, resolving the contradiction between flexibility and accuracy.
Solution Approach 2:
The system combines multiple types of image guides (depth, normal, semantic, edge) into a composite guidance framework. Each guide provides different structural information, and their combination enables more accurate and flexible pixel selection than any single guide could provide alone, addressing both the flexibility and accuracy requirements.
2Measurement precision
If conventional inpainting systems attempt to generate high-resolution results, then the resolution matches modern image capturing devices, but the systems fail to generate inpaintings that accurately reflect a scene at high resolutions
Solution Approach 1:
The system segments the inpainting process into multiple stages: generating multiple candidate inpaintings at high resolution using different image guides, then selecting the best candidate. This segmentation allows high-resolution processing while maintaining accuracy through multi-guided candidate generation and selection.
Solution Approach 2:
The system uses multiple image guides as feedback mechanisms to evaluate and select the best inpainting candidate. The depth map, normal map, semantic map, and edge map provide continuous feedback on how well each candidate preserves scene structure, enabling accurate high-resolution inpainting by selecting candidates that best satisfy all guide constraints.
3Device complexity
If a single inpainting result is generated using conventional methods, then the process is simple, but the system lacks the ability to make subtle comparisons and contrasts between candidates
Solution Approach 1:
The system generates multiple candidate inpaintings (excessive action) rather than a single result, then selects the best one. This approach sacrifices some computational simplicity but dramatically improves selection quality by enabling subtle comparisons between multiple candidates using multiple image guides.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media that implement an inpainting framework having computer-implemented machine learning models to generate high-resolution inpainting results. For instance, in one or more embodiments, the disclosed systems generate an inpainted digital image utilizing a deep inpainting neural network from a digital image having a replacement region. The disclosed systems further generate, utilizing a visual guide algorithm, at least one deep visual guide from the inpainted digital image. Using a patch match model and the at least one deep visual guide, the disclosed systems generate a plurality of modified digital images from the digital image by replacing the region of pixels of the digital image with replacement pixels. Additionally, the disclosed systems select, utilizing an inpainting curation model, a modified digital image from the plurality of modified digital images to provide to a client device.