Image Sensor Noise Reduction Using Pixel Distance Correction
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Solution Overview
Problem
Conventional noise reduction methods for image sensors often focus on a single type of noise and overlook image features, resulting in unsatisfactory noise reduction outcomes.
Innovation Solution
A system comprising an image sensor with a color filter array and a noise reduction device that corrects raw data based on distances between current and neighboring pixels, using different noise reduction schemes tailored to pixel types, and a color interpolation device to generate full-color data with reduced noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional noise reduction methods are used, then processing simplicity is maintained, but noise reduction effectiveness deteriorates because they focus on single noise type and overlook image features
Solution Approach 1:
The patent segments the noise reduction process into distinct stages: bad pixel removal, green channel noise reduction, and RGB noise reduction. Each stage targets specific noise types with dedicated algorithms, improving overall effectiveness while maintaining manageable complexity through modular processing
Solution Approach 2:
The patent applies different noise reduction schemes based on local image characteristics. It identifies edges and textures to adjust processing strength locally, preserving important image features while reducing noise in appropriate regions. The green channel processing uses local variance to adaptively determine noise reduction intensity
2Reliability
If single noise type reduction is applied, then processing simplicity is maintained, but comprehensive noise reduction deteriorates because distinct noise characteristics are overlooked
Solution Approach 1:
The patent creates a universal noise reduction system that handles multiple noise types (bad pixels, Gaussian noise, green imbalance, flicker noise) through a unified processing framework. The system adapts different algorithms based on the detected noise characteristics, achieving comprehensive coverage while maintaining systematic organization
Solution Approach 2:
The patent changes processing parameters based on the type of noise detected. Different sigma values, kernel sizes, and algorithmic approaches are applied depending on whether the noise is Gaussian, green channel imbalance, or flicker noise, allowing optimal reduction for each noise type
3Manufacturing precision
If image features are ignored in noise reduction, then processing simplicity is maintained, but image quality deteriorates because important features may be lost
Solution Approach 1:
The patent performs preliminary analysis of image features (edges, textures, local variance) before applying noise reduction. This allows the system to identify regions where noise reduction should be applied and regions where it should be avoided, preserving important image features while reducing noise elsewhere
Solution Approach 2:
The patent uses feedback from local image characteristics (variance, edge detection results) to adjust the noise reduction process. Regions with high variance or detected edges receive different processing than uniform regions, ensuring image features are preserved while noise is reduced in appropriate areas
Data Source
AI summary
In a system and method of reducing noise, an image sensor with a color filter array (CFA) outputs raw data, and a noise reduction device corrects the raw data according to distances between an original current pixel and neighboring same-color pixels in a process mask, thereby generating a new current pixel so as to output corrected raw data. A color interpolation device couples to receive the corrected raw data to result in full-color data.