Adaptive Image Noise Correction Using Weighted Pixel Averaging
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Solution Overview
Problem
Conventional image noise correction methods often degrade image quality by applying filters to regions with fine details and fail to correct noise in uniform but noisy areas due to difficulties in determining accurate thresholds.
Innovation Solution
An adaptive image noise correction method that calculates a weighted average of pixel values within a window, where each pixel is assigned a weighting factor based on its distance from the target pixel, eliminating the need for predefined thresholds and ensuring more weight is given to pixels in uniform areas and less to those in detailed areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a low-pass noise reduction filter is applied to the entire image, then noise is reduced, but image quality degrades due to blurring in detailed areas
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on local characteristics. It calculates a noise detection value for each pixel by comparing it with neighboring pixels, and only applies noise reduction filtering when the detected noise exceeds a threshold. This ensures that detailed areas with high frequency variations are preserved while noisy uniform regions are corrected.
Solution Approach 2:
The patent uses dynamic thresholding where the noise detection threshold is adapted based on local image characteristics. The threshold is determined by analyzing the statistical properties of pixel values in the neighborhood, allowing the filter to dynamically adjust its sensitivity to noise versus detail preservation based on the local content.
2Manufacturing precision
If threshold-based noise filtering is used, then detailed areas are preserved, but uniform noisy regions are not corrected due to inaccurate threshold determination
Solution Approach 1:
The patent replaces the conventional mechanical thresholding approach with a statistical analysis method. Instead of using a fixed or manually determined threshold, it calculates the noise detection value by comparing each pixel with its neighbors and determining whether the difference exceeds a dynamically computed threshold based on local variance, thereby adapting to different image regions automatically.
Solution Approach 2:
The patent implements a feedback mechanism where the noise detection process continuously monitors pixel values and their variations. The threshold is adjusted based on the observed statistical properties of the image regions, creating a closed-loop system that adapts to the actual noise characteristics present in different parts of the image.
Data Source
AI summary
An image noise correction method is provided. For at least one target pixel having a determined pixel value, for each pixel in a window of pixels surrounding the target pixel, a weighting factor for the pixel is estimated based on the value of the target pixel and at least one pixel value in the window. An average of pixel values for the pixels in the window is calculated, with each pixel value being weighted by the weighting factor corresponding to the pixel. A new value is assigned to the target pixel based on the average of pixel values that is calculated. Also provided is an image noise correction device.