Dynamic Image Processing Sequence for Feature-Specific Units
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Solution Overview
Problem
Existing image processing systems that use multiple units trained for different features face challenges such as lowered accuracy, degraded training efficiency, and quality issues when handling images with combinations of features, as the sequence of applying these units significantly affects the final processing result.
Innovation Solution
An image processing device with multiple units trained for different features, a decision unit to determine the optimal sequence based on the input image's features, and an application unit to apply these units in the decided sequence, ensuring optimal processing quality by configuring the image processing mechanism with a sequence planning unit and image processing control unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple image processing units trained for different features are applied in a fixed sequence, then the processing can be completed efficiently, but the quality and accuracy of the final result deteriorates when the fixed sequence is not optimal for the specific image features
Solution Approach 1:
The patent implements dynamic sequence selection by training a neural network to predict the optimal processing sequence based on the input image's feature composition. Instead of using a fixed sequence, the system adapts the sequence dynamically according to the specific image characteristics, thereby resolving the contradiction between processing efficiency and quality by selecting the most appropriate sequence for each image.
Solution Approach 2:
The system changes the parameter of processing sequence based on the input image's feature composition. By using a neural network to predict the optimal sequence order according to the detected features, the system adjusts the processing parameters (sequence order) to match the specific image characteristics, thus improving quality without sacrificing efficiency.
2Device complexity
If a single image processing unit is trained to handle all multiple features, then the system structure is simplified, but the accuracy and generalization capability deteriorates
Solution Approach 1:
The patent divides the image processing task into multiple specialized processing units, each trained to handle specific features. This segmentation allows each unit to develop expertise in its designated feature type, improving overall accuracy and generalization capability while maintaining a manageable system structure through modular design.
Solution Approach 2:
The system achieves multi-functionality by combining multiple specialized processing units that can be flexibly selected and sequenced based on the input image's features. The universal sequence prediction mechanism coordinates these specialized units, allowing the system to handle various feature combinations effectively without requiring each unit to be universally competent.
3Adaptability or versatility
If additional training is performed on a single image processing unit to improve its capability, then the training time and computational resources increase significantly
Solution Approach 1:
By segmenting the training task into multiple specialized units, each unit can be trained independently on its specific feature type. This avoids the exponential increase in training complexity that would occur if a single unit tried to learn all features, thereby reducing overall training time and computational resource requirements.
Solution Approach 2:
The system performs preliminary classification of image features to determine which specialized processing units are needed. This preliminary action allows the system to activate only the necessary units for each image, avoiding the need to continuously improve a single unit's capability across all possible features, thus reducing training overhead.
Data Source
AI summary
An image processing device includes multiple image processing units, each trained to accommodate a different feature possibly contained in an image, a decision unit that decides a sequence of the multiple image processing units according to the features contained in an input image, and an application unit that applies the image processing units to the input image in the sequence decided by the decision unit.