Neural ODE Brightness Adjustment for Image Signal Processors
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Solution Overview
Problem
Existing image brightness adjustment methods using neural networks are limited by biased and inefficient supervised learning due to limited data sets, failing to adapt effectively to real-world conditions.
Innovation Solution
An image signal processor employs a neural ordinary differential equation (ODE) network model for unsupervised learning, adjusting pixel values based on a loss function to generate optimal trajectories for brightness adjustment, enabling efficient and unbiased learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning with preset data sets is used to improve brightness adjustment accuracy, then learning accuracy is improved, but learning results become biased and learning efficiency is limited due to data set constraints
Solution Approach 1:
The system performs unsupervised learning by automatically learning from the input image data itself without requiring external preset data sets. The neural ODE network model learns the optimal brightness adjustment trajectories directly from the data, enabling the system to serve itself and adapt to any input image without being constrained by pre-collected training data.
2Adaptability or versatility
If more data sets are collected to improve learning coverage, then learning completeness is improved, but data collection time and computational resources increase
Solution Approach 1:
The unsupervised learning approach eliminates the need for external data collection by learning directly from the input images. The neural ODE network model extracts learning information on-the-fly from the pixel values and their transformations, converting the data collection process into a self-service operation that occurs during normal image processing rather than requiring separate data gathering phases.
3Speed
If traditional neural networks are used for brightness adjustment, then processing speed is maintained, but learning efficiency is limited due to supervised learning constraints
Solution Approach 1:
The patent transforms the learning paradigm from supervised to unsupervised by changing the fundamental parameter of learning methodology. The neural ODE network model uses continuous transformation parameters and optimal trajectory concepts to achieve efficient learning without the bottlenecks of supervised learning, while maintaining processing speed through the mathematical framework of ordinary differential equations that can be solved efficiently.
Data Source
AI summary
Provided is a method of operating an image signal processor, the method including receiving a plurality of first pixel values, calculating a plurality of first adjustment functions corresponding to the plurality of first pixel values, respectively, based on a neural ordinary differential equation (ODE) network model configured to generate, based on a pixel value, an adjustment function which is an optimal trajectory corresponding to the pixel value, generating a plurality of first adjusted pixel values corresponding to the plurality of first pixel values, respectively, based on the plurality of first adjustment functions, and learning the neural ODE network model based on a loss function on the basis of the plurality of first pixel values and the plurality of first adjusted pixel values, the adjustment function being an optimal trajectory in an adjustment section corresponding to a number of adjustment repetitions for adjusting the pixel value.