Image Processing Device Histogram Background Noise Removal
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Solution Overview
Problem
Existing image processing methods struggle to efficiently achieve a uniform background in images, especially in microscope images with varying background pixel values, which hinders accurate detection and requires manual input of background pixel values and time-consuming processing to distinguish between background and observation objects.
Innovation Solution
An image processing device generates a histogram of pixel values, sets a background pixel value based on the peak frequency, calculates a noise range, and replaces pixel values within this range with a single arbitrary value, allowing for a uniform background without altering the apparent color and reducing processing time by eliminating the need to determine pixel ownership.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual input of background pixel values and time-consuming processing methods are used to distinguish background from observation objects, then accurate background detection can be achieved, but processing time increases significantly
Solution Approach 1:
The system automatically determines background pixel values by generating histograms from image data and identifying peak values, eliminating the need for manual input. The processor autonomously performs background detection by analyzing pixel value distributions and replacing background pixels based on calculated thresholds, making the system self-sufficient in background characterization.
Solution Approach 2:
The invention changes the approach from manual parameter input to automatic parameter derivation. By transforming image data into histogram data and analyzing peak values, the system dynamically determines background pixel values based on the actual image content, adapting to varying lighting conditions and sample characteristics without manual intervention.
2Measurement precision
If complex processing methods are used to distinguish background from observation objects, then accurate background removal can be achieved, but device complexity increases
Solution Approach 1:
The invention extracts only the essential information needed for background detection by generating histograms that represent pixel value distributions. By focusing on peak values in the histogram rather than analyzing every pixel individually, the system simplifies the processing while maintaining accurate background identification and removal.
Solution Approach 2:
The system transforms the complex task of individual pixel analysis into a simpler histogram analysis problem. By changing the representation from raw pixel data to histogram data with peak values, the invention reduces computational complexity while preserving the ability to accurately distinguish background from observation objects.
3Stability of the object's composition
If background pixel values are not uniformly handled, then image detail is preserved, but background uniformity is poor
Solution Approach 1:
The system applies different processing strategies to different pixel types based on their characteristics. Background pixels identified through histogram analysis are uniformly replaced with the determined background pixel value, while observation object pixels retain their original values. This local differentiation achieves background uniformity without losing observation object details.
Solution Approach 2:
The invention creates a uniform background by copying the determined background pixel value to all identified background pixels. This copying process ensures consistency across the background region while the histogram-based identification method preserves the uniqueness and details of observation object pixels that fall outside the background value range.
Data Source
AI summary
An image processing device is configured to: generate a histogram of pixel values of a plurality of pixels contained in an image; set a background pixel value by using a peak value of the generated histogram; set a noise range with respect to the set background pixel value; and replace the pixel values that fall in the set noise range with a single arbitrary pixel value.