Dynamic Threshold Image Area Division for Noise Reduction
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Solution Overview
Problem
Existing image processing methods are susceptible to noise and fail to accurately divide smooth areas in images due to the use of fixed thresholds and inadequate inhibition of areas with indistinct features.
Innovation Solution
A method and apparatus that dynamically adjust thresholds for each pixel difference value, allowing for accurate division of image areas by reading pixel values, calculating differences, and comparing these differences with dynamically generated thresholds, thereby enhancing the distinctiveness of pixel comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed threshold method combined with differential operators is used to divide image areas, then the area division can be performed, but the method is highly sensitive to noise and produces general results with unclear levels among image areas
Solution Approach 1:
The patent applies dynamics by replacing fixed thresholds with dynamic thresholds that adapt to local image characteristics. The threshold for each pixel is calculated based on the standard deviation of grayscale values in its neighborhood, allowing the threshold to vary spatially according to the local noise level and image structure, thereby reducing noise sensitivity while maintaining division accuracy
Solution Approach 2:
The patent changes the parameter from a fixed threshold value to a dynamically calculated threshold based on local statistical properties (standard deviation). This parameter transformation allows the thresholding criterion to adapt to different regions of the image, improving both noise robustness and the distinctiveness of divided areas
2Measurement precision
If a fixed threshold method is used for area division, then the process is simple, but smooth areas with indistinct features cannot be accurately divided
Solution Approach 1:
The patent transforms the threshold parameter from a constant value to a locally adaptive value calculated from the standard deviation of grayscale intensities in the neighborhood of each pixel. This allows smooth areas to be divided by detecting subtle variations that a fixed threshold would miss, while the calculation remains computationally feasible through efficient standard deviation computation
3Reliability
If image area growing and combination methods are used, then adjacent areas can be merged based on statistical comparisons, but small areas with indistinct features cannot be inhibited leading to high noise susceptibility
Solution Approach 1:
The patent applies local quality by evaluating each pixel's threshold criterion locally based on its specific neighborhood statistics rather than applying a global threshold. This local evaluation allows small noisy areas to be identified and excluded from growth processes, as they will not meet the locally adapted threshold criterion, thereby improving reliability while reducing noise interference
Data Source
AI summary
A method and an apparatus for dividing an image area are provided. The method includes: a reading step: reading a pixel value of a current pixel and pixel values of multiple adjacent pixels adjacent to the current pixel in an image; a calculation step: calculating a difference between the pixel values of the current pixel and each adjacent pixel, so as to obtain a pixel difference value set, the pixel difference value set including multiple pixel difference values; a processing step: comparing each pixel difference value with a corresponding dynamic threshold in sequence, and determining all adjacent pixels in a same sub-image area as the current pixel, the current pixel and all the adjacent pixels of the current pixel in the same sub-image area constituting one sub-image area; and a traversal step: traversing each pixel in the image, and performing the reading step, the calculation step, and the processing step on each pixel in sequence, so as to acquire multiple sub-image areas of the image. Therefore, a smooth area can be accurately divided from an image.