Vignetting Correction via Radial Sector Analysis
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Solution Overview
Problem
Existing digital imaging systems face challenges in correcting for color and intensity vignetting, particularly in CMOS image sensors with small pixel sizes, as current methods require complex calibration across varying light sources and focal lengths, and are difficult to embed in image sensors due to their complexity and need for specific uniformly lit scenes.
Innovation Solution
A method and apparatus that partitions image pixels into radial sectors, computes average color values, converts them to logarithm space, applies a median filter to identify color gradients, selects gradients within a threshold, estimates lens model parameters, and updates the model to correct for vignetting, using a processing circuit to perform these operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional vignetting correction methods are used, then vignetting correction can be achieved, but the system requires complex calibration across different light sources and focal lengths
Solution Approach 1:
The image is divided into multiple radial sectors extending from the center pixel, with each sector processed independently to compute color gradients. This segmentation allows the system to handle vignetting correction locally across different regions without requiring global calibration for all lighting conditions
Solution Approach 2:
The system uses the image data itself to automatically estimate lens model parameters through median filtering and gradient computation, eliminating the need for external calibration scenes or manual calibration procedures. The algorithm self-calibrates by analyzing color gradients within the image
2Reliability
If traditional vignetting correction methods are used, then vignetting correction can be achieved, but the method requires specific uniformly lit calibration scenes
Solution Approach 1:
The system performs self-calibration by computing color gradients from the image data itself using median filters. It automatically estimates lens model parameters without requiring any external calibration equipment, uniform lighting setups, or manual intervention, making the process entirely autonomous and applicable to any image
Solution Approach 2:
The algorithm transforms the problem from requiring fixed calibration parameters to dynamically estimating parameters from image data. By computing color gradients and applying median filtering, the system adapts parameters based on the actual image content rather than relying on pre-determined calibration values
3Ease of manufacture
If other correction approaches are used, then single image correction is achieved, but the method requires image segmentation and large buffer which is too complex to embed in image sensor
Solution Approach 1:
The image is divided into radial sectors that can be processed independently with minimal data storage requirements. Each sector's color gradients are computed locally using median filtering, avoiding the need for large buffers or complex global segmentation algorithms that would be difficult to embed in sensor hardware
Solution Approach 2:
The patent replaces complex mechanical or computational segmentation systems with a mathematical approach using polar coordinate transformation and radial sector division. This substitution enables the correction algorithm to be implemented efficiently in embedded sensor hardware through straightforward mathematical operations rather than complex processing pipelines
Data Source
AI summary
A method and apparatus for correcting for vignetting include associating each pixel in the two-dimensional array with a pair of polar coordinates referenced to a preselected origin pixel and partitioning the two-dimensional array of image pixels into a plurality of sectors. For each sector, the method includes computing an average R value, an average G value and an average B value; converting the average R value, the average G value and the average B value for each sector to logarithm space; comparing color gradients along a radial sector line to a gradient threshold; selecting gradients that do not exceed the threshold; using the selected gradients, estimating parameters of a model of a lens which produced the image; and, using the parameters, updating the model of the lens and correcting the image.