GAN-Based Color Handle Generation for Digital Image Gradients
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Solution Overview
Problem
Conventional image processing systems require manual user interaction to specify color handles and their locations for generating color gradients, which is inefficient and challenging, especially for complex gradients, leading to computational inefficiencies and user frustration.
Innovation Solution
A neural network trained using a generative adversarial network (GAN) system generates candidate color handles and selects associated colors automatically, eliminating the need for manual input by employing a discriminator module to evaluate and refine the candidate handles through loss functions like pixel loss and adversarial loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user interaction is used to specify color handles and locations for generating color gradients, then the user can have control over the gradient generation process, but the process becomes inefficient and computationally intensive due to repeated manual interactions required to achieve desired results
Solution Approach 1:
The system uses machine learning models to automatically generate color handles and their locations based on the digital image content, eliminating the need for manual user interaction. The model autonomously determines optimal color handle placements and color values, making the system self-sufficient while maintaining high visual consistency with existing image elements.
2Reliability
If manual user interaction is required to specify color handles consistent with existing artwork, then visual consistency can be achieved, but the complexity of the operation increases especially for complex color gradients requiring multiple color handles
Solution Approach 1:
The patent replaces the manual mechanical process of selecting and placing color handles with an automated machine learning system. The ML model analyzes the digital image and automatically determines color handle locations and colors, substituting human manual operations with computational processes that maintain visual consistency without operational complexity.
3Manufacturing precision
If repeated manual interactions are performed to achieve desired gradient results, then accurate color gradients can be generated, but computational inefficiencies arise due to the iterative nature of manual adjustments
Solution Approach 1:
The system performs preliminary analysis of the digital image using machine learning models to pre-determine optimal color handle locations and color values before gradient generation. This preliminary action eliminates the need for repeated iterative adjustments, achieving accurate gradients in a single pass and reducing computational time significantly.
Data Source
AI summary
Color handles are generated based on digital image color gradients using machine learning. In order to train a neural network of a generator module of a GAN system, a training digital image is used by the neural network of the generator module to generate a candidate color handle. The discriminator module of the GAN system then compares the candidate color handle with a ground truth color handle of the training digital image using a loss function as part of machine learning, and uses a result of this comparison to train the neural network of the generator module.