Image data pre-processing for neural networks
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Solution Overview
Problem
Neural networks face computational inefficiencies due to the need to process image data with varying noise levels and loss of exposure information, requiring them to perform noise level prediction and exposure determination, which increases network complexity and introduces errors.
Innovation Solution
An image signal processor (ISP) that separates and processes pixel data and characteristic data in parallel, applying corresponding operations to maintain exposure and noise information, reducing the computational burden on neural networks by providing optimized input data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the neural network processes raw image data with varying noise levels and exposure information, then the neural network can perform comprehensive image analysis, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the image processing task into two distinct parts: (1) an image signal processor that performs noise reduction and exposure correction on raw image data, and (2) a neural network that receives the pre-processed data for analysis. This segmentation allows each component to specialize, reducing the overall complexity of the neural network while maintaining analysis accuracy.
Solution Approach 2:
The image signal processor performs preliminary actions (noise reduction and exposure correction) on the raw image data before it is fed into the neural network. By pre-processing the data to correct exposure variations and reduce noise, the neural network receives cleaner, standardized input, which reduces its computational complexity and processing requirements.
2Adaptability or versatility
If the neural network processes image data with noise and exposure variations, then it can handle real-world image conditions, but the processing time and computational resources increase
Solution Approach 1:
The image signal processor performs preliminary noise reduction and exposure correction on raw image data before it is fed into the neural network. By pre-processing the data to correct exposure variations and reduce noise, the neural network receives cleaner, standardized input, which reduces its computational complexity and processing requirements, thereby improving processing speed.
Solution Approach 2:
The image signal processor automatically adjusts noise levels and exposure parameters in the image data before processing, making the data suitable for neural network analysis without requiring the neural network itself to perform these adjustments. This self-service approach handles real-world image conditions efficiently.
3Reliability
If the neural network performs noise level prediction and exposure determination, then it can compensate for image quality variations, but the network size and computational burden increase
Solution Approach 1:
The patent segments the processing tasks by having the image signal processor handle noise reduction and exposure correction separately from the neural network's analysis functions. This segmentation eliminates the need for the neural network to perform noise level prediction and exposure determination, reducing its size and computational energy requirements while maintaining processing reliability through the dedicated pre-processing stage.
Data Source
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Figure 2
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AI summary
An image processor (100) comprising an input module (120) for receiving image data (110) from an image sensor; an image processing module (130) arranged to perform one or more operations on at least a portion of the image data (110) to generate processed image data (160i); and a characteristic processing module (140) arranged to perform one or more characteristic processing operations on at least a portion of the characteristic data to generate processed characteristic data (160n). The portion of the characteristic data is associated with the portion of the image data (110); the one or more characteristic processing operations are associated with the one or more image processing operations. The image processor (100) further comprises an output module (150) for outputting the processed image data (160i) and processed characteristic data (160n).