Noise Estimation Model Training via Synthetic Image Generation
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Solution Overview
Problem
Existing machine learning models for image noise estimation require extensive training data captured under various shooting parameters, limiting accuracy due to restricted noise types and ISO settings, making it inefficient to obtain diverse noise images.
Innovation Solution
A method and system that generates additional training images by modifying the noise levels of initial images captured under specific parameters, using algorithms to simulate various noise conditions, allowing for improved noise estimation models with reduced data capture requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive training data is captured under various shooting parameters, then model accuracy is improved, but data collection time and complexity increase
Solution Approach 1:
The patent generates synthetic training images by copying and modifying existing images through controlled noise addition algorithms. Instead of capturing new images under various parameters, the system creates virtual training data by applying different noise models to existing images, thereby reducing data collection time while maintaining training accuracy.
Solution Approach 2:
The system changes parameters of existing images by adding simulated noise with varying characteristics (different noise types, intensities, and patterns) to create diverse training data. This allows the model to learn noise estimation across multiple scenarios without physically recapturing images under different shooting parameters.
2Ease of operation
If images are captured under limited shooting parameters, then data collection is simplified, but noise estimation accuracy deteriorates
Solution Approach 1:
The patent creates synthetic training images by copying existing images and applying controlled noise transformations. This allows the system to generate diverse noise scenarios from a single image source, maintaining data collection simplicity while achieving comprehensive noise estimation training.
Solution Approach 2:
The system introduces an intermediary noise simulation layer between the original image and the training data. By inserting controlled noise models as an intermediary step, the system can generate realistic noise variations without requiring actual captures under different shooting parameters, thus maintaining simplicity while improving accuracy.
3Ease of manufacture
If noise images are captured with fixed ISO and camera settings, then capture process is simplified, but training data diversity is reduced
Solution Approach 1:
The patent applies parameter changes by modifying image characteristics through algorithmic noise addition rather than changing camera settings. The system can generate images with diverse noise characteristics (different types, intensities, and distributions) while maintaining the same capture process simplicity, thereby achieving high training data diversity without sacrificing capture ease.
Solution Approach 2:
The system copies existing images and applies virtual noise transformations to create diverse training data. This copying approach allows unlimited diversity in training images without requiring actual captures under different ISO and camera settings, maintaining capture simplicity while achieving maximum data versatility.
Data Source
AI summary
An image processing apparatus and method is provided and includes one or more processors and one or more memory devices that store instructions. When the instructions are executed by the one or more processors, the one or more processors are configured to perform operations including obtaining a first image which is obtained by capturing based on a first shooting parameter, generating, by image processing on the first image, a second image corresponding to a second shooting parameter which is different from the first shooting parameter and using at least the generated second image as a training image for a model that is used in noise estimation processing for an input image.