Neural Image Encoding And Editing With Reconstruction Loss Correction

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Solution Overview

Problem

Conventional image editing solutions face a trade-off between ensuring the quality of attribute editing and the reconstruction process, resulting in significant differences between the generated and original images, leading to poor editing effects.

Innovation Solution

A method involving multiple neural network models to encode and edit images, where loss information is obtained from the difference between encoded and original images, and the edited images are corrected to minimize reconstruction loss, using pre-trained encoders and generators to generate a more realistic transformed image.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional neural network models are used to encode and edit images, then attribute editing can be performed, but reconstruction loss increases causing significant differences between generated and original images

Engineering Contradiction:

Improveattribute editing capabilityVSAvoidimage reconstruction quality

Core Design Contradiction:

Adaptability or versatilityVSManufacturing precision

Solution Approach 1:

The patent segments the image processing into two distinct components: an encoder that extracts features and a generator that reconstructs images. By separating the editing function (in encoder) from the reconstruction function (in generator), the system can modify attributes while preserving overall image quality through the generator's optimization to minimize reconstruction loss.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements a feedback mechanism where reconstruction loss is calculated by comparing generated images with original images, and this loss information is used to optimize the generator. This closed-loop feedback allows the system to learn and minimize reconstruction errors, improving image quality while maintaining editing capabilities.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If encoding and editing operations are performed on original images, then attribute transformation is achieved, but reconstruction loss occurs reducing editing effectiveness

Engineering Contradiction:

Improveattribute transformation capabilityVSAvoidediting effect quality

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent introduces an intermediary generator component that acts as a bridge between the encoded features and the final reconstructed image. This generator mediates the transformation process by learning to reconstruct high-quality images from edited features, thereby reducing reconstruction loss and improving the reliability of editing effects without limiting attribute transformation capabilities.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250336125A1Method, device, storage medium and program product for image generation

Publication Date: 2025.10.30 BEIJING ZITIAO NETWORK TECH CO LTD

AI summary

The embodiments of the present disclosure of the present disclosure provides a method, device, electronic device, computer storage medium, computer program product and computer program of image generation. The method comprises: obtaining original image; processing the original image to generate a first image and a second image, wherein the first image is an image generated by encoding the original image, and the second image is an image generated by encoding and editing the original image; obtaining loss information based on the first image and the original image; and generating a target transform image by correcting the second image based on the loss information.