Residual Detail Latent Code for Image Editing
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Solution Overview
Problem
Conventional image generating systems using neural networks often lose details in high-frequency areas, require significant computational resources, and introduce unwanted visual artifacts, making them inefficient and inflexible for real-time image editing.
Innovation Solution
An image detail enhancement system that iteratively updates a residual detail latent code for high-frequency areas using a neural network encoder, combining it with the edited latent code to improve details in the resulting image, thereby enhancing features like hair and wrinkles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional neural network systems are used to edit images in latent space, then image modification capability is achieved, but high-frequency details are lost
Solution Approach 1:
The patent segments the latent code into two distinct components: a coarse latent code representing low-frequency information and a residual latent code representing high-frequency details. This segmentation allows independent processing and preservation of detail information that would otherwise be lost in conventional latent space editing.
Solution Approach 2:
The patent introduces a residual encoder as an intermediary component that extracts and preserves high-frequency residual information from the original image. This residual encoder acts as a mediator between the coarse latent code and the final reconstructed image, ensuring detail preservation without interfering with the main editing process.
2Productivity
If conventional latent space editing is performed, then image editing speed is improved, but computational costs remain high
Solution Approach 1:
The patent extracts only the essential residual detail information needed for high-quality reconstruction, rather than processing the entire image data. By taking out and separately handling only the high-frequency residual components, the system reduces overall computational load while maintaining editing speed.
Solution Approach 2:
The patent applies partial action by focusing computational resources only on the residual detail components rather than reprocessing the entire image. This selective processing approach reduces computational costs while maintaining the speed benefits of latent space editing.
3Adaptability or versatility
If conventional image generating systems are used, then image modification is achieved, but unwanted visual artifacts are introduced
Solution Approach 1:
The patent performs preliminary encoding of the residual detail information before the main editing process. By pre-extracting and storing the residual components, the system prepares the detail information in advance, allowing it to be seamlessly integrated after editing without introducing artifacts.
Solution Approach 2:
The patent combines the edited coarse latent code with the preserved residual latent code to create a composite representation. This composite approach merges the flexibility of latent space editing with the detail preservation of residual information, eliminating visual artifacts that would result from either approach alone.
4Manufacturing precision
If detailed areas are enhanced in edited images, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent merges the coarse latent code processing with the residual latent code integration in a unified framework. By combining these two components into a single enhancement process, the system achieves detail improvement without proportionally increasing system complexity.
Solution Approach 2:
The residual encoder serves multiple functions: it extracts high-frequency details, preserves them in latent form, and enables their integration into edited images. This multi-functionality reduces the need for separate specialized components, thereby limiting the increase in device complexity while maintaining high detail enhancement quality.
Data Source
AI summary
Methods, systems, and non-transitory computer readable media are disclosed for intelligently enhancing details in edited images. The disclosed system iteratively updates residual detail latent code for segments in edited images where detail has been lost through the editing process. More particularly, the disclosed system enhances an edited segment in an edited image based on details in a detailed segment of an image. Additionally, the disclosed system may utilize a detail neural network encoder to project the detailed segment and a corresponding segment of the edited image into a residual detail latent code. In some embodiments, the disclosed system generates a refined edited image based on the residual detail latent code and a latent vector of the edited image.