Image Model Training With Signal-Weighted Loss for Gamma Shifts
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Solution Overview
Problem
Existing image processing methods using machine learning models are affected by gamma correction, leading to decreased estimation accuracy due to changes in signal values during image development, particularly in high-resolution tasks like deblurring and upsampling.
Innovation Solution
A method that calculates a weighting coefficient based on the signal values of training, ground truth, and output images to minimize errors, incorporating gamma correction considerations during training, thereby stabilizing estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used for image processing tasks like deblurring and upsampling, then processing capability is improved, but estimation accuracy decreases due to gamma correction effects on signal values
Solution Approach 1:
The patent applies parameter changes by introducing signal-value-dependent weighting coefficients into the loss function. These coefficients dynamically adjust the importance of different pixel regions based on their signal values, compensating for gamma correction effects. By changing the weighting parameters according to signal magnitude, the model maintains accurate estimation across different image regions despite gamma correction transformations.
Solution Approach 2:
The patent implements feedback mechanisms during training by continuously monitoring the relationship between input images, predicted outputs, and ground truth images. The weighting coefficients are calculated based on signal values from these images and fed back into the loss function calculation. This feedback loop enables the model to adapt to gamma correction effects and maintain estimation accuracy throughout the training process.
2Measurement precision
If weighting coefficients are introduced to compensate for signal value changes, then estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent changes parameters by computing weighting coefficients based on signal values from the input image, ground truth image, and predicted output image. These coefficients are then integrated into the loss function, allowing the model to automatically adjust its behavior based on signal magnitude without requiring complex architectural modifications. This parameter-based approach maintains relative simplicity while improving accuracy.
Solution Approach 2:
The weighting coefficients act as intermediaries between the image data and the loss function. Rather than directly modifying the model architecture or adding complex processing steps, the patent introduces these intermediate weighting factors that bridge the gap between raw signal values and the loss calculation. This intermediary approach simplifies the overall system while achieving the desired accuracy improvement.
Data Source
AI summary
A method for generating a trained model includes acquiring a training image and a ground truth image, inputting the training image into a machine learning model to generate an output image, acquiring a weighting coefficient, calculating a loss using the ground truth image, the output image, and the weighting coefficient, and updating a parameter of the machine learning model based on the loss. The weighting coefficient changes according to at least one of a signal value of the training image, a signal value of the ground truth image, and a signal value of the output image.