CFA Color Space Conversion With Pixel-Wise Noise Suppression
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Solution Overview
Problem
Existing image processing systems struggle to effectively convert images captured by color filter arrays (CFAs) with wider color spectrums into images with narrower color spectrums while minimizing noise and maintaining image quality.
Innovation Solution
A trained color conversion model is used to determine a bias and conversion matrix for each pixel, converting images from a first CFA with a wider color spectrum to a second CFA with a narrower spectrum, incorporating demosaicing and noise suppression techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If color space conversion is performed from a first CFA with wider color spectrum to a second CFA with narrower spectrum, then image quality and fidelity are improved, but noise is amplified and image clarity deteriorates
Solution Approach 1:
The patent applies demosaicing operation before color space conversion to generate a full sampling image. This preliminary action ensures that all color channel information is available for each pixel before the conversion process, which helps maintain image quality while managing noise amplification during the CFA transformation.
Solution Approach 2:
The patent introduces a trained color conversion model as an intermediary between the first CFA and second CFA transformations. This neural network-based mediator learns optimal conversion parameters (conversion matrices and biases) from training data, enabling accurate color space transformation while suppressing noise amplification that would otherwise occur in traditional conversion methods.
2Productivity
If traditional color conversion methods are used to convert between different CFA color spaces, then the conversion process is simple and fast, but image fidelity and color accuracy deteriorate
Solution Approach 1:
The color conversion model is pre-trained on a large dataset of training images with ground truth labels before deployment. This preliminary training phase allows the model to learn optimal conversion parameters offline, so that during actual image processing, the conversion can be performed quickly using the learned conversion matrices and biases without sacrificing image fidelity.
Solution Approach 2:
The patent transforms the color conversion process from fixed parameter transformations to adaptive parameter selection. The trained model determines optimal conversion matrices and biases dynamically based on the input image characteristics, allowing the system to maintain high image fidelity while achieving fast conversion speeds through efficient neural network inference.
Data Source
AI summary
An apparatus with image processing includes: one or more processors configured to: generate a full sampling image by demosaicing a first color filter array (CFA)-based input image; based on a trained color conversion model trained for color conversion and noise suppression of the full sampling image, determine a bias and a conversion matrix corresponding to each pixel of the full sampling image; and based on the conversion matrix and the bias, convert a color space of the full sample image, corresponding to the first CFA, into a color space corresponding to a second CFA.