Generative Neural Network Image Extension
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Solution Overview
Problem
Existing image processing technologies struggle to efficiently extend images beyond their original borders while preserving high-level semantic characteristics and low-level structures and textures.
Innovation Solution
A system utilizing a generative neural network trained with an adversarial loss objective function to generate extended images by processing input images and predicting realistic extensions, including additional rows and columns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of moving object
If existing image processing technologies are used to extend images beyond original borders, then image extension capability is achieved, but the ability to preserve high-level semantic characteristics and low-level structures and textures deteriorates
Solution Approach 1:
The patent introduces a discriminative neural network as an intermediary component that works in conjunction with the generative neural network. This discriminative network evaluates the realism of generated image extensions by determining whether extended regions appear authentic, providing feedback that guides the generative network to preserve semantic characteristics and textures while extending the image area.
Solution Approach 2:
The patent implements an adversarial training framework where the discriminative neural network provides feedback to the generative neural network. The discriminative network evaluates generated image extensions and provides loss signals that guide the generative network to improve its output, ensuring that extended regions maintain semantic consistency and visual fidelity with the original image.
2Speed
If traditional image extension methods are applied, then processing speed is maintained, but the realism and quality of extended images deteriorates
Solution Approach 1:
The patent replaces traditional mechanical or algorithmic image extension methods with a neural network-based generative system. The generative neural network, trained through adversarial learning, substitutes conventional image processing algorithms and produces more realistic extensions by learning complex patterns and structures from training data, thereby improving image realism while maintaining acceptable processing speeds.
3Device complexity
If simple image extension algorithms are used, then computational complexity is reduced, but the ability to maintain structural and textural integrity deteriorates
Solution Approach 1:
The patent transforms the approach to image extension by changing the fundamental parameters of the system - using trained neural network models with learned parameters instead of simple algorithms with fixed rules. The generative and discriminative networks contain numerous trainable parameters that are optimized through adversarial training, enabling the system to maintain high structural and textural integrity in extended regions while managing computational complexity through efficient network architectures.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating realistic extensions of images. In one aspect, a method comprises providing an input that comprises a provided image to a generative neural network having a plurality of generative neural network parameters. The generative neural network processes the input in accordance with trained values of the plurality of generative neural network parameters to generate an extended image. The extended image has (i) more rows, more columns, or both than the provided image, and (ii) is predicted to be a realistic extension of the provided image. The generative neural network is trained using an adversarial loss objective function.