Proxy-Guided Image Editing for Artifact-Free Object Removal
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Solution Overview
Problem
Conventional image generation systems struggle with accurately removing objects from high-resolution images, often introducing artifacts or replacing objects with incorrect ones, and require significant computational resources.
Innovation Solution
A system utilizing a first image generation model trained to generate proxy guidance based on low-resolution inputs, which is then used by a second image generation model to produce high-resolution synthetic images with precise object removal, leveraging a teacher model's distilled knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional image generation system processes high-resolution images directly, then the output image quality may be maintained, but the computational resources required increase significantly and artifacts are introduced
Solution Approach 1:
The patent segments the image processing task into two distinct stages: first processing a low-resolution version of the image to generate initial editing guidance, then using that guidance to process the high-resolution image. This segmentation allows the computationally intensive operations to be performed on smaller data first, reducing overall computational resource requirements while maintaining final image quality.
Solution Approach 2:
The system performs preliminary processing on low-resolution images before tackling high-resolution images. By generating proxy guidance from low-resolution inputs first, the system prepares editing instructions in advance that can then be applied efficiently to the high-resolution image, avoiding the need to process the full high-resolution data from scratch.
2Productivity
If conventional systems remove objects from high-resolution images, then the removal task is completed, but artifacts are introduced and incorrect replacements occur
Solution Approach 1:
The patent introduces an intermediary proxy guidance image at low resolution as a mediator between the input image and the final high-resolution output. This intermediary contains the essential editing information and removal instructions that guide the processing of the high-resolution image, ensuring accurate object removal without introducing artifacts or incorrect replacements.
Solution Approach 2:
The system creates a low-resolution copy of the input image to generate proxy guidance. This copy serves as a surrogate for performing the initial object identification and removal planning, allowing the system to determine what needs to be removed from the high-resolution image without directly processing it at full quality, thereby improving removal accuracy.
3Use of energy by moving object
If low-resolution processing is used to reduce computational demands, then computational resources are reduced, but image quality and detail are lost
Solution Approach 1:
The patent adds a temporal dimension to the processing by separating operations into sequential stages: first low-resolution processing to generate guidance, then high-resolution processing to produce the final output. This dimensional separation allows the system to exploit the computational efficiency of low-resolution processing while preserving the quality benefits of high-resolution processing in the final stage.
Solution Approach 2:
The system applies different quality levels to different stages of processing: low-resolution for the proxy guidance generation stage where fine detail is less critical, and high-resolution for the final output stage where quality is paramount. This local quality approach optimizes computational resource usage at each stage while ensuring the final result meets quality requirements.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining an input image and an input mask, wherein the input mask indicates a region of the input image to be modified and generating, using a first image generation model, an intermediate result based on the input image and the input mask, wherein the intermediate result modifies the region of the input image indicated by the input mask. A second image generation model generates a synthetic image based on the input image and the intermediate result, wherein the synthetic image depicts the input image with content from the modified region at a higher level of detail than the intermediate result.