AI Filter Parameter Control for Adaptive Image Enhancement
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Solution Overview
Problem
Conventional image enhancement technologies in electronic devices are costly and inflexible, requiring separate hardware configurations for each image enhancement element and making it difficult to update image processing technology quickly, as they are fixed at the manufacturing stage and require users to manually select enhancement modes.
Innovation Solution
An electronic device equipped with an AI model trained using a deep neural network (DNN) that identifies and adjusts parameters for multiple filters, such as noise reduction, sharpness enhancement, and contrast correction, to optimize image processing based on the input image and user environment, allowing for real-time adjustments and reduced hardware complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If separate hardware configurations are used for each image enhancement element, then image enhancement quality is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent combines multiple separate image enhancement hardware configurations into a single integrated AI model that processes images through multiple filters (noise reduction, sharpness enhancement, contrast correction, etc.). This consolidation maintains comprehensive image enhancement capabilities while reducing hardware complexity and manufacturing costs.
Solution Approach 2:
The AI model serves as a universal image enhancement system that can perform multiple enhancement functions (noise reduction, sharpness, contrast, color correction) through a single multi-functional processor, replacing the need for separate dedicated hardware for each enhancement element.
2Stability of the object's composition
If image enhancement technology is fixed at manufacturing stage, then hardware stability is improved, but adaptability and updateability deteriorate
Solution Approach 1:
The patent implements dynamic image enhancement by using an AI model that can be updated and retrained with new data, allowing the system to adapt to changing requirements and new enhancement techniques without hardware changes. The model parameters can be adjusted dynamically based on input image characteristics.
Solution Approach 2:
The system enables technology updates by changing the parameters and weights within the AI model through software updates and retraining, rather than requiring hardware modifications. This allows continuous improvement of image enhancement capabilities while maintaining hardware stability.
3Measurement precision
If users manually select enhancement modes, then control precision is improved, but ease of operation deteriorates
Solution Approach 1:
The AI model performs automatic image enhancement by analyzing input images and selecting appropriate filter parameters without requiring user intervention. The system serves itself by making intelligent decisions about which enhancements to apply and to what degree, based on the characteristics of each input image.
Solution Approach 2:
The system uses feedback from image analysis to automatically adjust enhancement parameters. The AI model evaluates the input image characteristics and dynamically determines the optimal enhancement settings, creating a closed-loop system that adapts to each image's specific needs without manual user input.
Data Source
AI summary
The present disclosure relates to an artificial intelligence (AI) system that utilizes a machine learning algorithm, and applications therefore. Disclosed is an electronic device. The electronic device comprises: a storage unit which stores therein an artificial intelligence model trained to determine parameters for a plurality of filters used for image processing on the basis of a deep neural network (DNN); and a processor for determining, through the artificial intelligence mode, parameters for each of the plurality of filters used for image processing for an input image, and performing, through the plurality of filters, filtering of the input image on the basis of the determined parameters so as to perform image processing for the input image.