Guided Upsampling for High-Resolution Image Inpainting
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Solution Overview
Problem
Conventional image inpainting methods struggle to produce semantically reasonable results, especially in high-resolution images, as they often lead to over-smoothed results due to convolutional structure layers losing image detail information, and optimization processes to preserve details are computationally inefficient.
Innovation Solution
An image inpainting system using guided upsampling, which borrows high-resolution feature patches from a high-resolution input image based on patch similarities computed using a low-resolution inpainting result, ensuring that pixel information synthesized in a high-resolution inpainting result remains semantically plausible and visually realistic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image inpainting methods use convolutional structure layers to process high-resolution images, then the inpainting task can be performed, but the results become over-smoothed and lose image detail information
Solution Approach 1:
The patent segments the image processing task into two distinct stages: (1) low-resolution inpainting to obtain semantic structure, and (2) high-resolution detail restoration using patch matching. This segmentation allows each stage to focus on specific aspects - semantic understanding at low resolution and detail preservation at high resolution - thereby resolving the contradiction between producing reliable inpainting results and preserving image detail information
Solution Approach 2:
The patent performs preliminary inpainting on a down-sampled version of the image before restoring details. By first completing the semantic structure at low resolution and then using patch matching to transfer high-resolution details, the system preliminarily establishes the inpainted structure before adding fine details, preventing information loss that would occur if high-resolution processing attempted to handle both semantics and details simultaneously
2Loss of information
If optimization processes are applied to preserve image details during inpainting, then image detail information can be maintained, but the computational efficiency decreases significantly
Solution Approach 1:
The patent divides the computationally intensive task into two phases: a lightweight low-resolution inpainting phase that handles semantics efficiently, and a detail restoration phase that uses patch matching from the original high-resolution image. This segmentation avoids applying heavy optimization processes to the entire high-resolution image, thereby maintaining computational efficiency while preserving details
Solution Approach 2:
The patent uses patch matching to copy high-resolution detail patches from the original image to the inpainted regions. Instead of computationally expensive optimization to generate details, the system copies existing high-quality patches that match the semantic context, achieving detail preservation with significantly lower computational cost
3Manufacturing precision
If inpainting is performed directly on high-resolution images, then the final output resolution is achieved, but the computational cost and processing time increase
Solution Approach 1:
The patent segments the resolution handling into two levels: low-resolution processing for semantic inpainting (fast) and high-resolution patch copying for detail restoration (efficient). This segmentation allows the system to achieve high output resolution without the computational burden of processing the entire high-resolution image through complex inpainting algorithms
Solution Approach 2:
The patent performs preliminary inpainting computations on a down-sampled version of the image, which requires significantly less processing time. The high-resolution output is then achieved by copying patches from the original image rather than computing them from scratch, thereby reducing processing time while maintaining high output resolution
4Extent of automation
If convolutional neural networks are used for image inpainting, then the task can be automated, but the network structure complexity increases
Solution Approach 1:
The patent segments the automated inpainting process into two simpler components: (1) a low-resolution neural network for semantic understanding and (2) a patch matching algorithm for detail restoration. This segmentation reduces the complexity of any single network component while maintaining high automation, as each component has a more focused and simpler function compared to a single complex high-resolution inpainting network
Data Source
AI summary
Methods and systems are provided for accurately filling holes, regions, and/or portions of high-resolution images using guided upsampling during image inpainting. For instance, an image inpainting system can apply guided upsampling to an inpainted image result to enable generation of a high-resolution inpainting result from a lower-resolution image that has undergone inpainting. To allow for guided upsampling during image inpainting, one or more neural networks can be used. For instance, a low-resolution result neural network (e.g., comprised of an encoder and a decoder) and a high-resolution input neural network (e.g., comprised of an encoder and a decoder). The image inpainting system can use such networks to generate a high-resolution inpainting image result that fills the hole, region, and/or portion of the image.