Personalized Image Quality Optimization via Deep Learning
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Solution Overview
Problem
Current imaging technologies face challenges in providing personalized image quality evaluation and optimization due to the lack of a standard description for image quality, making it difficult for users to articulate preferences and determine optimal parameter settings, which often requires time-consuming trial-and-error processes.
Innovation Solution
A learning-based framework that uses deep learning techniques to identify raw image quality features, apply user preferences to determine target image quality features, and adjust processing parameters to optimize image quality, incorporating a feedback interface for user input and iterative refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image quality optimization is implemented, then productivity and resource efficiency are improved, but the system requires complex deep learning models and extensive training data infrastructure
Solution Approach 1:
The system performs preliminary actions by pre-training deep learning models on extensive datasets before deployment. The model is trained in advance to recognize image quality features and predict optimal parameters, so that during actual operation, optimization can be performed rapidly without real-time complex computations.
Solution Approach 2:
The patent introduces an intermediary feedback interface that mediates between the automated optimization system and the end user. This interface allows users to provide preference feedback which is then processed by the system to refine predictions, acting as a bridge that simplifies user interaction while maintaining system intelligence.
2Adaptability or versatility
If personalized image quality evaluation is implemented, then adaptability to individual user preferences is improved, but the system requires gathering and processing extensive user feedback data
Solution Approach 1:
The system applies local quality by focusing on extracting specific, relevant features from user feedback rather than processing all possible image parameters. The feedback interface collects targeted preference information that is then used to refine predictions for that specific user's local preferences, rather than attempting to model all possible quality dimensions.
Solution Approach 2:
The system changes parameters by using the collected feedback data to adjust and refine its prediction models over time. As more user feedback is gathered, the system modifies its internal parameters and weighting schemes to better match individual user preferences, enabling personalization without requiring overwhelming amounts of data storage.
3Manufacturing precision
If deep learning models are used for parameter determination, then manufacturing precision of image quality is improved, but the training time and computational resources are increased
Solution Approach 1:
The system performs the computationally intensive model training as a preliminary action before deployment. The deep learning models are trained in advance on large datasets to learn the complex relationships between image parameters and quality metrics, so that during actual use, the models can quickly make accurate predictions without requiring real-time training computations.
Data Source
AI summary
A computer-implemented method for providing image quality optimization individualized for a user includes a computer receiving raw image data acquired from an image scanner and identifying one or more raw image quality features based on the raw image data. The computer automatically determines one or more target image quality features by applying one or more user preferences to the one or more raw image quality features. The computer also automatically determines one or more processing parameters based on the one or more target image quality features. The computer may then process the raw image data using the one or more processing parameters to yield an image.