Image Registration for Realistic Neural-Modified Image Merging

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Solution Overview

Problem

Existing image processing systems struggle to seamlessly combine modified image portions with original images due to issues like warping, scaling, and shading, making it difficult to achieve a realistic and precise integration.

Innovation Solution

Implementing image registration techniques using neural networks to align and transform modified subimages with reference images, employing methods such as dense and sparse registration, and various transforms like similarity, affine, and projective transforms, to ensure precise alignment and blending.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If neural networks modify images to achieve desired characteristics (warping, scaling, shading, animation), then image generation capability is improved, but the ability to combine modified portions with original images deteriorates due to disparities and unrealistic appearance

Engineering Contradiction:

Improveimage generation capabilityVSAvoidcombination realism

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system performs image registration and transformation operations before combining the modified subimage with the reference image. By pre-aligning the modified portion with the original image using registration algorithms and applying appropriate transformations, the system eliminates disparities and ensures seamless integration, resolving the contradiction between image modification capability and combination realism

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary registration and transformation process between the modified subimage and the reference image. This intermediary step includes determining registration parameters, calculating transformations, and applying corrections to align the modified portion with the original image before final combination, thereby ensuring realistic appearance while maintaining image generation versatility

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If image modifications are applied to subimages, then image processing precision is improved, but alignment accuracy with reference image deteriorates due to warping and scaling effects

Engineering Contradiction:

Improveimage processing precisionVSAvoidalignment accuracy

Core Design Contradiction:

Measurement precisionVSManufacturing precision

Solution Approach 1:

The system performs registration and transformation calculations before final image combination. By pre-determining the appropriate transformation parameters and applying them to align the modified subimage with the reference image, the system maintains both processing precision and alignment accuracy, eliminating warping and scaling disparities

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent changes parameters such as transformation matrices, registration parameters, and alignment coefficients to compensate for warping and scaling effects. By adjusting these parameters dynamically based on the modified subimage characteristics, the system maintains alignment accuracy while preserving image processing precision

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12541865B2Image processing with image registration to facilitate combination of images modified by neural networks with reference images

Publication Date: 2026.02.03 NVIDIA CORP

AI summary

In various examples, systems and methods are disclosed relating to registering image processing with image registration for image generation and content stream applications. Systems and methods are disclosed for registering portions of images that are modified to incorporate content or features, with references images from which the portions of the images are identified. The systems and methods can transform the modified portions to more realistically and precisely merge back into the reference images, such as for presentation as a content stream.