Endoscope Pixel Correction via Statistical Dark Frame Analysis
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Solution Overview
Problem
Flickering defective pixels in solid-state image sensors used in endoscopes generate irregular image noise due to random telegraph signal noise, which existing methods struggle to effectively detect and correct in real-time.
Innovation Solution
A processing device and method that determines if pixel values are below a threshold, accumulates data over frames, calculates statistical values, and corrects pixel values outside a preset range to identify and correct flickering defective pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images are captured with the image sensor shielded from light to detect flickering defective pixels, then detection accuracy is improved, but processing time and complexity increase
Solution Approach 1:
The patent applies preliminary action by capturing multiple dark images (with the lens cap attached) before actual imaging to pre-detect and identify flickering defective pixels. This preliminary detection phase separates the defective pixel identification from the actual imaging process, allowing the system to flag problematic pixels in advance. The statistical analysis of dark images establishes a baseline for normal pixel behavior, enabling real-time correction during subsequent imaging without requiring repeated dark frame captures.
2Reliability
If statistical analysis is performed on accumulated pixel values to identify flickering defective pixels, then detection reliability is improved, but computational complexity increases
Solution Approach 1:
The patent employs parameter changes by analyzing the statistical distribution of pixel values across multiple dark images. Specifically, it calculates the standard deviation or variance of pixel values for each pixel position across the captured dark frames. Flickering defective pixels exhibit abnormally high statistical variation compared to normal pixels. By transforming the problem from qualitative visual inspection to quantitative statistical parameter analysis, the system achieves reliable detection with computationally efficient operations that can be implemented in real-time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Reliably detects and corrects flickering defective pixels in real-time, improving image quality by reducing irregular noise in endoscope images.
Implementation Method 1
a solid state image sensor, such as a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS), includes a plurality of pixels that receives light and performs photoelectric conversion to output electric signals
Data Source
AI summary
A processing device includes: a processor including hardware, the processor being configured to acquire image data; determine, for each pixel of an image corresponding to the acquired image data, whether a pixel value of the pixel is equal to or less than a preset threshold as a dark level; accumulate, for a predetermined number of frames, the pixel value that has been determined to be equal to or less than the preset threshold and positional information regarding a position of the pixel whose pixel value has been determined to be equal to or less than the preset threshold, on the image sensor; calculate a statistical value of the accumulated pixel values for each pixel; determine, for each pixel, whether the statistical value falls outside a preset range; and correct a pixel value of the pixel whose statistical value has been determined to fall outside the preset range.