Semantic Code Vector Inpainting for Artifact Reduction
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Solution Overview
Problem
Convolutional neural networks often produce poor results when removing unwanted content from digital images due to their inability to selectively utilize correct semantic classes, leading to ambiguity artifacts and suboptimal editing outcomes.
Innovation Solution
The system generates semantic code vectors for different portions of an image, using a semantic encoder and decoder to precisely apply contextual information, avoiding convolution operations and reducing artifacts by selectively using relevant semantic classes for inpainting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If convolutional neural networks are used to fill replacement content into regions, then the editing function can be performed, but visible artifacts are generated and results are poor
Solution Approach 1:
The patent segments the image into multiple portions and generates semantic codes for each portion separately. This segmentation allows the system to process and replace content in specific regions independently, improving inpainting accuracy by avoiding the artifact generation problem of convolutional neural networks while maintaining the ease of unwanted content removal function.
Solution Approach 2:
The patent applies local quality by generating semantic codes specifically for different portions of the image based on semantic information. Each portion receives tailored semantic codes relevant to its content, enabling precise replacement without generating artifacts, thus resolving the contradiction between operational ease and inpainting accuracy.
2Productivity
If convolutional neural networks are used for inpainting, then replacement content can be generated, but ambiguity artifacts are produced
Solution Approach 1:
The patent extracts semantic information from the image and generates semantic codes that capture the essential characteristics of different portions. By taking out only the relevant semantic data and using it for inpainting, the system achieves fast content replacement without generating ambiguity artifacts, eliminating the harmful effect while maintaining productivity.
Solution Approach 2:
The patent introduces semantic codes as an intermediary between the image and the replacement content. These semantic codes mediate the inpainting process by providing precise semantic guidance, enabling fast content replacement without the ambiguity artifacts that would otherwise be generated by direct convolutional approaches.
3Manufacturing precision
If semantic codes are generated for different portions of the image, then inpainting precision is improved, but processing complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-processing the image to generate semantic information and semantic codes for different portions before the actual inpainting process. This preliminary segmentation and coding step simplifies the subsequent inpainting operation, achieving high precision while managing processing complexity through structured preparation.
Data Source
AI summary
An inpainting method includes retrieving image information at an electronic device, where the image information identifies an area within an image. The method also includes retrieving, using the electronic device, semantic information including a plurality of semantic classes and a semantic class distribution for each semantic class of the plurality of semantic classes. The method further includes generating semantic codes associated with different portions of the image based on the image information and the semantic information. In addition, the method includes constructing the area within the image by generating image content based on the semantic information.