Dual Neural Network Architecture for Real-Time Image Processing
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Solution Overview
Problem
Consumer devices with limited computational power and battery life face challenges in processing complex data such as images and videos in real-time, limiting the effectiveness of artificial intelligence techniques like neural networks for improving image quality and efficiency.
Innovation Solution
A novel signal processing architecture that combines two or more neural networks or data paths within a neural network to process complex input signals, where one network detects features and the other performs filtering or processing, with outputs combined to selectively apply filtering functions only to detected features, reducing computational load and enhancing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single neural network is used to perform both feature detection and filtering functions, then the device complexity is reduced, but the processing speed and efficiency deteriorate due to the computational load
Solution Approach 1:
The neural network is segmented into two separate networks: a first neural network dedicated to feature detection and a second neural network dedicated to filtering operations. This segmentation allows each network to specialize in its respective function, improving processing efficiency while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The outputs of the first and second neural networks are merged through element-wise multiplication to produce the final processed output. This combining operation integrates the feature detection results with the filtering operations, achieving both speed and accuracy improvements.
2Manufacturing precision
If comprehensive filtering is applied to all regions of an image, then image quality improvement is enhanced, but the computational resources and energy consumption increase significantly
Solution Approach 1:
The system applies filtering selectively only to regions where features are detected by the first neural network, rather than applying comprehensive filtering to the entire image. This local quality approach concentrates computational resources on areas that benefit most from processing, improving image quality where needed while reducing overall energy consumption.
Solution Approach 2:
Instead of applying full filtering across the entire image, the system performs partial filtering only on detected feature regions. This partial action strategy achieves sufficient image quality improvement for the most important areas while avoiding the excessive computational cost of processing every pixel uniformly.
3Productivity
If complex AI processing is performed on consumer devices, then image and video processing capability is improved, but battery life is reduced due to high computational power requirements
Solution Approach 1:
By segmenting the processing into feature detection and filtering stages with separate neural networks, the system optimizes computational efficiency. This allows complex AI processing to be performed with reduced redundant calculations, lowering power consumption and extending battery life while maintaining advanced image processing capabilities.
Solution Approach 2:
The system changes the processing parameters by applying filtering only to detected feature regions rather than all pixels. This parameter change reduces the total number of computational operations required, decreasing energy consumption and preserving battery life while still achieving high-quality image processing results.
Data Source
AI summary
A first neural network generates a first output tensor based on an input tensor, the first output tensor comprising values to impart an effect to one or more features in the input tensor. A second neural network generates a second output tensor based on the input tensor, and the effect to be imparted to the one or more features is modulated, based at least in part, on the second output tensor.