US20260350745
2026-11-05
Physics
G06T3/4046
The patent application outlines a system and method for image processing, specifically focusing on image extension or "uncropping" using neural networks. It involves obtaining an image with an initial aspect ratio and identifying a target aspect ratio that differs from the initial one. The image is then positioned within a frame that accommodates the target aspect ratio, with additional regions outside the original image boundaries. A generative neural network creates extended portions that seamlessly integrate with the original image, extending scene elements depicted in the image.
Image processing, particularly image extension, has become crucial in fields like photography and computer vision. Traditional image processing tools often require specialized skills, making them less accessible to non-experts. Current methods for image extension are typically complex, involving multiple steps and technical expertise. This complexity presents a barrier for users, highlighting the need for more intuitive and efficient image processing techniques that maintain image quality and integrity.
The application proposes a user-friendly image processing system that automates the extension of images to fit a user-specified aspect ratio. This system fills in content around the original image, adjusting it for various applications such as social media or digital art. The system also supports advanced features like image dilation to reduce boundary artifacts and text-guided content filling, enhancing the customization of extended images.
By simplifying the image extension process, the system broadens its usability to include professional photographers, graphic designers, and hobbyists. It allows users to efficiently generate extended images without extensive technical knowledge. The system's interface facilitates easy adjustments to extended regions, ensuring high-quality outputs that meet user specifications. This approach significantly improves upon existing methods by offering a more accessible and effective solution for image manipulation.
The system includes components such as a user device, server, and cloud infrastructure to process user inputs and generate extended images. Users can specify a target aspect ratio and provide text inputs for content guidance. The system uses a generative neural network to create extended image portions, integrating them with the original image to produce a cohesive extended image. This process is managed by a diffusion-based text-guided model, which supports a range of aspect ratios and automatically creates masks based on user inputs, ensuring precise and high-quality image extensions.