Machine Learning Model for Image Resolution Enhancement
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Solution Overview
Problem
Existing image processing technologies struggle to enhance image quality, particularly in terms of resolution and visual clarity, without increasing hardware complexity or cost.
Innovation Solution
The use of training data comprising combined single images with pixel shifting and enhanced resolution to optimize a trained model for improving image quality, allowing for higher resolution and clarity without additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing image processing technologies are used to enhance image quality, then image resolution and visual clarity can be improved, but hardware complexity and cost increase
Solution Approach 1:
The patent replaces complex hardware systems with a software-based machine learning model. Instead of using additional sensors, lenses, or processing hardware to improve image quality, the invention uses a trained neural network that processes standard images to generate super-resolution outputs. This substitution of mechanical/optical systems with computational algorithms resolves the contradiction by achieving high-resolution enhancement without increasing hardware complexity
Solution Approach 2:
The patent changes the parameter of image resolution through software processing rather than hardware modification. The machine learning model transforms low-resolution input images into high-resolution output images by learning complex mappings from training data, effectively changing the resolution parameter without requiring hardware that supports higher native resolutions
2Manufacturing precision
If existing image processing technologies are used to enhance image quality, then visual clarity can be improved, but hardware cost increases
Solution Approach 1:
The invention substitutes expensive hardware upgrades with a software model that can be deployed on existing devices. The trained machine learning model provides high-quality image enhancement capabilities without requiring costly hardware modifications, making the technology economically viable for widespread implementation
Solution Approach 2:
The patent creates a computational copy of the image enhancement function through the machine learning model. Instead of physically upgrading hardware to achieve better image quality, the system uses software to replicate and enhance the visual information, providing high-quality outputs without the cost of hardware upgrades
3Manufacturing precision
If multiple single images are combined to create training data with enhanced resolution, then the trained model can generate higher resolution images, but the processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-processing multiple single images to create high-quality training data before model training. The training data is prepared in advance with combined and enhanced resolution images, allowing the model to learn from pre-processed examples. This preliminary preparation simplifies the actual inference process, as the trained model can then generate high-resolution images through a single forward pass without requiring complex real-time processing
Data Source
AI summary
Training data is used for machine learning of a model. The training data includes a correct answer image obtained by combining a plurality of single images, and an example image representing the plurality of single images.