Chromatic Aberration Removal Model for Real-Time Image Processing
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Solution Overview
Problem
Chromatic aberration in camera lenses causes unwanted color fringing at the edges of captured images, particularly under high-dynamic range and backlight conditions, which existing image processing algorithms struggle to effectively remove due to misjudgment and color loss issues.
Innovation Solution
A deep convolutional neural network model trained on a data set of chromatic-aberration-free and chromatic-aberration images, using a self-adaptive loss function to focus on high-contrast areas, separates brightness and chromaticity signals, and corrects chromatic aberration without altering brightness information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional image processing algorithms are used to remove chromatic fringing, then chromatic aberration can be reduced, but misjudgment and color loss occur leading to poor removal效果
Solution Approach 1:
The patent replaces traditional mechanical/image processing algorithms with a deep learning neural network model. The model learns chromatic fringing patterns and removal strategies from training data, substituting rule-based processing with data-driven intelligent processing that adapts to various imaging conditions without misjudgment or color loss.
Solution Approach 2:
The patent performs preliminary training of the neural network model using extensively annotated training data that includes various chromatic fringing scenarios. This preliminary learning phase enables the model to acquire robust chromatic fringing removal capabilities before actual image processing, improving both accuracy and reliability in practical application.
2Manufacturing precision
If deep learning model is used for chromatic aberration removal, then removal accuracy is improved, but processing speed may decrease affecting real-time requirements
Solution Approach 1:
The patent optimizes model parameters including network architecture, training hyperparameters, and inference settings to achieve the best balance between accuracy and speed. The model structure and training parameters are specifically tuned to enable real-time processing while maintaining high chromatic fringing removal accuracy.
Solution Approach 2:
The patent segments the image processing task into distinct computational stages within the neural network model, allowing parallel processing and optimization of each segment. This segmentation enables the model to process images efficiently while maintaining high accuracy in chromatic fringing removal.
3Loss of information
If brightness and chromaticity are processed together, then complete image information is preserved, but chromatic aberration correction becomes complex and less effective
Solution Approach 1:
The patent segments the image into brightness and chromaticity components, processing each separately through dedicated neural network branches. The brightness component handles luminance information while the chromaticity component handles color information, including chromatic fringing. This segmentation simplifies the processing complexity while preserving complete image information through subsequent integration of both components.
Solution Approach 2:
The patent introduces an intermediary processing stage where brightness and chromaticity components are handled separately before final integration. This intermediary separation allows independent optimization of each component's processing, reducing overall system complexity while maintaining complete information preservation through the merging of processed components.
Data Source
AI summary
An image processing method and apparatus, a model training method and apparatus, and a storage medium. The image processing method includes: acquiring a to-be-processed image; and inputting the to-be-processed image into a chromatic aberration-removing model to obtain a target image output by the chromatic aberration-removing model, where the chromatic aberration-removing model is obtained by a training data set through training, the training data set includes multiple groups of sample data, and each group of sample data includes a chromatic-aberration-free image and a chromatic-aberration image corresponding to the chromatic-aberration-free image.