Neural Network Image Enhancement via Segmentation Maps
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Solution Overview
Problem
Existing methods for enhancing image aesthetics are time-consuming and often fail to accurately reflect the desired aesthetic, as they do not effectively account for the content of the image.
Innovation Solution
A neural network system trained using a combination of aesthetic enhancement and adversarial neural networks, which learns to conditionally generate enhanced images based on the content of input images, using segmentation maps to ensure context-aware adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual manipulation of image aspects is used to enhance aesthetics, then the user can control the enhancement process, but the process becomes time-consuming and tedious
Solution Approach 1:
The system enables automatic image enhancement by training a neural network to self-learn aesthetic improvements from training data, eliminating the need for manual user manipulation while maintaining high aesthetic accuracy through automated content-aware processing
Solution Approach 2:
The patent replaces manual mechanical editing operations with an automated neural network system that processes images through learned aesthetic transformations, substituting human manual control with intelligent automated processing that achieves both speed and precision
2Productivity
If generic image enhancement methods are used, then the processing is faster, but the enhancement does not accurately reflect the desired aesthetic for specific image content
Solution Approach 1:
The system applies different enhancement strategies to different regions of the image based on content segmentation, allowing each region to receive customized aesthetic processing that matches its specific content characteristics while maintaining overall processing efficiency through automated regional analysis
Solution Approach 2:
The neural network dynamically adjusts enhancement parameters based on the specific content and characteristics of each image region, transforming generic processing into content-adaptive enhancement that achieves both speed and aesthetic precision through learned parameter optimization
3Measurement precision
If content-based enhancement is implemented, then the aesthetic accuracy improves, but the system complexity increases due to multiple neural networks
Solution Approach 1:
The system segments the enhancement task into distinct functional neural network components (adversarial network for realism, aesthetic network for quality assessment, enhancement network for transformation), where each segment handles a specific aspect of content-aware enhancement, making the overall complex system manageable and effective
Solution Approach 2:
The patent introduces segmentation maps as intermediary elements that bridge the input image and the neural network processing, providing content-based guidance that enables accurate content-aware enhancement while structuring the system to handle complexity through organized intermediate representations
Data Source
AI summary
Methods and systems are provided for generating enhanced image. A neural network system is trained where the training includes training a first neural network that generates enhanced images conditioned on content of an image undergoing enhancement and training a second neural network that designates realism of the enhanced images generated by the first neural network. The neural network system is trained by determine loss and accordingly adjusting the appropriate neural network(s). The trained neural network system is used to generate an enhanced aesthetic image from a selected image where the output enhanced aesthetic image has increased aesthetics when compared to the selected image.