Diffusion Image Cropping for Clear Zoomed Object Views
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Solution Overview
Problem
Existing communication systems require users to invest significant resources and effort to create high-quality images, often resulting in missed opportunities for sharing and presenting objects due to the inability to fill in missing details during zoom operations, leading to blurry or distorted images.
Innovation Solution
A generative machine learning model, such as a diffusion model, is used to analyze images and generate artificial content that fills in missing features or improves the view of target objects, reducing the need for manual adjustments and enhancing image quality with minimal user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually create high-quality images, then image quality is improved, but time and resource expenditure increase
Solution Approach 1:
The system enables images to self-enhance through automated AI processing. When an image is uploaded, the system automatically detects objects, generates alternative views, and improves image quality without requiring manual user intervention for each enhancement step, thus reducing time and resource expenditure while maintaining high image quality
Solution Approach 2:
The patent replaces manual mechanical image creation and editing processes with automated AI-based generative models. Instead of users manually creating or editing images, the system uses machine learning algorithms to automatically generate high-quality images and alternative views, substituting human effort with computational processes
2Measurement precision
If users zoom into images, then detailed view is improved, but image clarity deteriorates due to missing details
Solution Approach 1:
The system performs preliminary generation of alternative views and detailed information before the user actually needs to zoom in. By pre-processing images to create multiple views and enhancing details in advance, the system ensures that when users zoom in, the detailed views are already prepared and available, maintaining image clarity at all zoom levels
Solution Approach 2:
The patent introduces an AI-based generative model as an intermediary between the original image and the zoomed-in view. This intermediary process fills in missing details and generates plausible content in regions that would otherwise be blurry or distorted, acting as a mediator that preserves image clarity during zoom operations
3Productivity
If users share images quickly, then communication efficiency is improved, but image quality may deteriorate
Solution Approach 1:
The system automatically enhances image quality through AI processing without requiring users to manually intervene or wait for lengthy processing steps. The automated object detection, alternative view generation, and image enhancement occur in the background, allowing users to share images quickly while the system simultaneously improves their quality, thus maintaining both communication efficiency and image quality
Solution Approach 2:
The system performs preliminary image enhancement and alternative view generation before the user initiates sharing. By pre-processing images to improve quality and generate multiple views in advance, the system ensures that high-quality images are ready for immediate sharing, eliminating the trade-off between speed and quality
Data Source
AI summary
Methods and systems are disclosed for enhancing or modifying an image by a diffusion model. The methods and systems receive a first image depicting a real-world scene including a target object and receive input associated with adjusting a zoom level of the first image. The methods and systems, in response to receiving the input, modify the zoom level associated with the first image to generate a second image having a view of the target object that is different from a view of the target object in the first image. The methods and systems analyze the second image using a generative machine learning model to generate an artificial image that modifies portions of the second image to improve the view of the target object relative to the second image.