Bayer Pattern Grid Noise Removal via Line Difference Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for removing grid noises in image processing systems, such as those using Gaussian or median filters, often damage image details like high-frequency edges or boundaries while attempting to eliminate grid noises caused by differences in green pixel averages between even and odd lines in Bayer pattern images.
Innovation Solution
A device and method that calculate average values, estimate values, and difference values between even and odd lines in a Bayer pattern image, then apply a correction coefficient, specifically averaging the difference and applying it by subtraction to odd lines and addition to even lines, to correct the image without damaging details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional filters (Gaussian or median filter) are used to remove grid noises, then grid noises are removed, but image details (high-frequency edge or boundary) are damaged
Solution Approach 1:
The patent applies different correction strategies to different parts of the image based on local characteristics. It calculates correction coefficients specifically for green pixels in Bayer pattern images, applying local quality adjustment rather than uniform filtering across the entire image, thus preserving edges while removing grid noises
Solution Approach 2:
The patent changes the approach from traditional filtering to parameter-based correction. It calculates average values of green pixels, determines correction coefficients based on these averages, and applies these coefficients to correct the image data, thereby removing grid noises while maintaining image details through parameter adjustment rather than convolution filtering
2Object-affected harmful factors
If conventional filters are applied to eliminate grid noises caused by difference in green pixel averages between even and odd lines, then the grid noises are removed, but the image quality deteriorates due to loss of high-frequency information
Solution Approach 1:
The patent performs preliminary calculation of average values for green pixels in even and odd lines before applying correction. By calculating correction coefficients in advance based on these averages, it prepares the correction data beforehand, enabling precise removal of grid noises while preserving image quality without needing aggressive post-filtering
Solution Approach 2:
The patent uses feedback by calculating the actual average values of green pixels from the input image, comparing even and odd line averages, and using this feedback to generate appropriate correction coefficients. This closed-loop approach ensures that correction is tailored to the specific image characteristics, maintaining reliability while removing grid noises
Data Source
AI summary
A device and a method for removing grid noises are disclosed. The device for removing grid noises in accordance with an embodiment of the present invention calculates an average value of each line of an inputted Bayer pattern image, calculates an estimate value estimating an average of even number lines placed between the odd number lines of a Bayer pattern image and odd number lines placed between the even number lines of a Bayer pattern image, calculates a difference value between the estimate value and the average value, calculates a correction coefficient by using the difference value, and applies the correction coefficient to the Bayer pattern image and outputs a corrected Bayer pattern image.