Automated Image Enhancement via Neural Network Feedback
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Solution Overview
Problem
Manual image enhancement processes are time-consuming and user-experience dependent, leading to variability in image quality and difficulty in reproducing consistent results across different users, as each user may apply different techniques and degrees of enhancement.
Innovation Solution
An automated method using a neural network, specifically a Double Dueling Deep Q Network (D3QN), analyzes image data to predict the effects of various enhancement processes, calculates reward values, and determines the necessary enhancements, thereby reducing human intervention and ensuring consistent quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual image enhancement processes are used, then user experience and expertise can guide enhancement decisions, but the process becomes time-consuming and produces variable results depending on the user
Solution Approach 1:
The system enables self-service by allowing the image data to automatically undergo enhancement processes without manual user intervention. The neural network autonomously analyzes the image, determines appropriate enhancements, and applies them, eliminating the need for user expertise while maintaining consistent results.
Solution Approach 2:
The patent replaces the mechanical system of manual user decision-making with an automated neural network-based system. The D3QN neural network substitutes human judgment and manual enhancement processes, providing consistent, reproducible results without the variability inherent in manual operations.
2Adaptability or versatility
If manual image enhancement processes are used, then flexibility in choosing enhancement techniques exists, but reproducibility of results across different users is difficult
Solution Approach 1:
The system implements feedback mechanisms where the neural network evaluates the original image, predicts enhancement outcomes, calculates reward values, and uses this feedback loop to determine the optimal enhancement sequence. This automated feedback ensures consistent decision-making across different images while maintaining adaptability through the neural network's learning capability.
Solution Approach 2:
The patent changes the parameter of decision-making from human subjective judgment to objective neural network predictions. The system uses predicted outcomes and reward values as parameters to automatically determine enhancement sequences, providing both adaptability through multiple enhancement options and reproducibility through consistent algorithmic decision-making.
3Productivity
If automated neural network processes are used, then preprocessing time is reduced and consistency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary action by pre-training the neural network on large datasets of images and enhancement outcomes. This pre-computed knowledge is stored in the neural network's parameters, allowing rapid inference during actual image enhancement without requiring complex real-time calculations, thus improving processing speed while managing system complexity.
Solution Approach 2:
The patent introduces the neural network as an intermediary between the raw image data and the enhancement processes. The D3QN neural network acts as a mediator that translates image features into enhancement decisions, simplifying the overall system architecture by centralizing the decision-making function in a single trainable component rather than requiring complex rule-based systems.
Data Source
AI summary
A method for automatically enhancing image data comprising receiving image data. Utilizing a neural network to analyze the image data to predict how each of a plurality of image enhancement processes will affect the image data. Utilizing the neural network to calculate a reward value for each of the plurality of enhancement processes that can be applied to the image data. Determining if the image data should be enhanced or not, wherein the determination is based on the predictions how each of the plurality of image enhancement processes will affect the image data. When it is determined that the image data should be enhanced, then determining which of the plurality of image enhancing process should be applied to the image data, wherein determination is based on the reward value for each of the plurality of enhancement processes. Applying the determined enhanced processes to the image data.