Neural Network Parameter Adaptation for Object-Specific Image Restoration
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Solution Overview
Problem
Existing learning-based image processing methods, such as upscaling, struggle to perform specialized restoration for objects with various features in an image, as they are trained to minimize overall error rather than reflecting object-specific features.
Innovation Solution
A display device and its operating method that modify the parameters of a neural network's layers based on the features of objects within an image, allowing for object-specific image processing by using model information that varies according to object features for each region and pixel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single neural network is used for image processing, then the overall error is minimized, but the features of each object are not reflected in the learning process
Solution Approach 1:
The patent segments the image into multiple regions based on object features, and applies different neural network models to different regions. This allows each region to be processed with a model optimized for its specific object characteristics, thereby reflecting object-specific features while maintaining overall error minimization.
Solution Approach 2:
The patent implements local quality by using different model parameters for different regions of the image. Each region's neural network model is customized based on the object features detected in that region, enabling the system to adapt to local characteristics rather than applying a uniform processing approach throughout the entire image.
2Quantity of substance
If complex training data is used to train a single network, then more object features are included, but the network is trained to minimize overall error rather than reflecting individual object features
Solution Approach 1:
Instead of using complex training data to train a single network, the patent segments the training process by creating multiple specialized models for different object types. Each model is trained on specific object features, allowing for precise object-specific restoration without being diluted by overall error minimization.
Solution Approach 2:
The patent changes the parameters of the neural network based on the detected object features in each region. By dynamically adjusting model parameters according to the specific object being processed, the system achieves high manufacturing precision for object-specific restoration while avoiding the need for excessively complex training data.
3Reliability
If a large-capacity, high-complexity network is used for upscaling, then image restoration performance is improved, but the structure becomes less adaptable to specialized restoration for different object classes
Solution Approach 1:
The patent divides the image processing task into multiple segments, each handled by a specialized neural network model optimized for specific object classes. This segmentation allows the system to maintain high restoration performance for each object type while being adaptable to different object classes, avoiding the limitations of a single large-capacity network.
Solution Approach 2:
The patent creates a universal system that can handle multiple object classes by using multiple specialized models. Each model is designed for a specific object class, but collectively they provide universal coverage for various object types in the image, achieving both high restoration performance and adaptability.
Data Source
AI summary
A display device for performing image processing by using a neural network including a plurality of layers, may obtain a plurality of pieces of model information respectively corresponding to pixels included in a first image based on object features respectively corresponding to the pixels; identify the plurality of pieces of model information respectively corresponding to the plurality of layers and the pixels input to the neural network based on information about a time point at which each of the pixels is processed in the neural network; update parameters of the plurality of layers, based on the plurality of pieces of model information; and obtain a second image by processing the first image via the plurality of layers to which the updated parameters are applied; and display the second image.