High-Resolution Image Inpainting with Coarse-to-Refined Upscaling
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Solution Overview
Problem
Existing image inpainting methods using neural networks often result in blurry, discolored, or inconsistent inpainted regions that are aesthetically unpleasing and less suitable for image processing operations, such as edge detection or object detection, due to limitations in resolution and refinement capabilities.
Innovation Solution
A framework that includes a super-resolution network between a coarse network and a refinement network to upscale the initial inpainted image, allowing the refinement network to refine unnatural details and achieve higher resolution inpainted regions compatible with the surrounding image features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional inpainting methods are used, then processing speed is maintained, but output resolution remains limited to lower resolutions
Solution Approach 1:
The image is divided into multiple patches, and the inpainting process is applied to each patch separately. This segmentation allows the model to process high-resolution details efficiently while maintaining overall image coherence through attention mechanisms that capture long-range dependencies across patches.
Solution Approach 2:
The patent transforms the traditional 2D image processing into a 3D volumetric representation by adding the depth dimension through multiple diffusion steps and attention layers. This dimensional expansion enables the model to capture complex spatial relationships and generate high-resolution details more effectively.
2Manufacturing precision
If image size is increased for higher resolution inpainting, then output quality improves, but computational complexity increases significantly
Solution Approach 1:
By segmenting the large image into smaller patches, the computational complexity is reduced from O(N²) to O(k·n²) where N is the total number of pixels, n is the number of pixels per patch, and k is the number of patches. This allows high-resolution processing without quadratic complexity growth.
Solution Approach 2:
The model performs preliminary downsampling to create a coarse representation of the entire image, which guides the subsequent detailed inpainting of individual patches. This preliminary action provides global context information that improves local patch reconstruction quality without requiring full-resolution processing throughout.
3Manufacturing precision
If diffusion steps are increased for better image quality, then inpainting accuracy improves, but processing time increases
Solution Approach 1:
The diffusion process is applied separately to each image patch rather than the entire image at once. This allows the model to use fewer diffusion steps per patch while achieving comparable overall quality, reducing total processing time while maintaining inpainting accuracy.
Solution Approach 2:
A preliminary coarse inpainting pass is performed first to establish the basic structure and content, followed by fewer refinement diffusion steps on the patches. This two-stage approach achieves high accuracy with fewer total diffusion iterations than uniform high-step processing would require.
Data Source
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AI summary
A method includes receiving an input image. The input image corresponds to one or more masked regions to be inpainted. The method includes providing the input image to a first neural network. The first neural network outputs a first inpainted image at a first resolution, and the one or more masked regions are inpainted in the first inpainted image. The method includes creating a second inpainted image by increasing a resolution of the first inpainted image from the first resolution to a second resolution. The second resolution is greater than the first resolution such that the one or more inpainted masked regions have an increased resolution. The method includes providing the second inpainted image to a second neural network. The second neural network outputs a first refined inpainted image at the second resolution, and the first refined inpainted image is a refined version of the second inpainted image.