Adaptive Image De-noising Preserving Contrast and Details
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Solution Overview
Problem
Current digital signal processing algorithms face challenges in effectively reducing noise in digital images, particularly in detail-rich areas and along edges and contours, often leading to information loss and blurring.
Innovation Solution
A system and method that utilize a distance calculator, weight calculator, and de-noised pixel calculator to compare pixel values and determine weights based on distances, allowing for the calculation of de-noised pixel values that reduce noise in uniform, edge, and detail-rich areas while preserving contrast.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional de-noising algorithms are applied to reduce noise in digital images, then noise reduction is achieved in uniform areas, but image details and contrast are lost in edge and contour regions
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on local characteristics. It identifies uniform areas, edge regions, and detail-rich areas, and applies appropriate de-noising techniques to each region separately, preserving local image quality while reducing noise globally
Solution Approach 2:
The patent segments the image into different regions (uniform areas, edge regions, detail-rich areas) and processes each segment differently. This segmentation allows the algorithm to preserve important image features in critical regions while still achieving noise reduction in less sensitive areas
2Object-affected harmful factors
If conventional de-noising algorithms are applied to eliminate noise, then noise levels decrease, but image contrast is reduced
Solution Approach 1:
The patent dynamically adjusts processing parameters based on local image characteristics such as contrast levels and noise intensity. By changing parameters adaptively rather than applying fixed thresholds, the algorithm preserves contrast in high-contrast regions while still achieving noise reduction in appropriate areas
3Object-affected harmful factors
If conventional de-noising algorithms are applied to reduce noise in detail-rich areas, then noise is decreased, but fine details are blurred
Solution Approach 1:
The patent applies different processing intensities to different image regions based on local detail content. In detail-rich areas, it uses more conservative de-noising parameters that preserve fine structures, while in uniform areas it applies stronger noise reduction
Solution Approach 2:
The patent uses dynamic thresholding and adaptive parameter selection that responds to local image characteristics. The processing strength varies dynamically across the image based on measured local variance and detail content, preventing blurring in detail-rich regions
Data Source
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AI summary
A system and method for reducing noise in images is disclosed. The present invention reduces noise and preserves contrast of an image to be displayed, the image having pixels, by (1) comparing a value of a first pixel to values of a set of other pixels; (2) comparing values of pixels neighboring the first pixel to values of further pixels neighboring the set of other pixels; (3) determining, for each pixel in the set of other pixels, a weight based on results of steps (1) and (2); (4) calculating a de-noised pixel value based on the weights of each pixel in the set of other pixels; and (5) replacing the value of the first pixel with the de-noised pixel value.