Currency Note Image Correction via Pixel Intensity Adjustment
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Solution Overview
Problem
Automated systems like ATMs and vending machines face challenges in determining note authenticity due to image distortion caused by improper illumination, centering, and lens debris, leading to false rejections or acceptances, which affects processing throughput and customer satisfaction.
Innovation Solution
A method for image processing that identifies and corrects pixel intensity values outside a predefined range by subdividing the image into smaller snippets, applying Modulation Transfer Function to determine correction factors, and adjusting pixel brightness and contrast to ensure uniform illumination, thereby creating a modified image for accurate authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the scanner lens has debris or the note is not properly centered, then image capture is still possible, but pixel intensity values become distorted leading to authentication errors
Solution Approach 1:
The patent applies preliminary action by correcting pixel intensity values before authentication processing. The system identifies pixels with intensity values outside the expected range and corrects them using algorithms that reference surrounding pixels and note characteristics, ensuring accurate authentication data is prepared in advance.
Solution Approach 2:
The patent converts the harmful effect of distorted pixel intensities into a benefit by using the distortion patterns themselves as indicators. The correction algorithm analyzes which pixels fall outside expected ranges and uses this information to selectively adjust only those pixels, transforming the problem of distortion into a targeted correction process that improves overall authentication accuracy.
2Ease of operation
If the note is not properly illuminated or centered, then image capture can still proceed, but authentication accuracy decreases due to incorrect pixel intensities
Solution Approach 1:
The patent implements feedback by continuously monitoring pixel intensity values during image processing and comparing them against expected ranges. When pixels are identified with incorrect intensity values, the system applies corrective algorithms that reference surrounding pixels and note characteristics, creating a feedback loop that ensures authentication decisions are based on corrected, accurate data rather than distorted original values.
Solution Approach 2:
The patent applies parameter changes by modifying pixel intensity values to fall within expected ranges. The correction algorithm adjusts individual pixel parameters based on their deviation from normal values, using mathematical models that consider spatial relationships and note characteristics to restore accurate intensity values for authentication processing.
3Productivity
If pixel intensity values are not corrected, then processing is faster, but false rejections and acceptances increase
Solution Approach 1:
The patent applies preliminary action by performing pixel intensity correction before authentication processing. The system identifies and corrects distorted pixels in advance, ensuring that the authentication algorithms receive clean, accurate data. This preliminary correction prevents authentication errors while maintaining processing efficiency.
Solution Approach 2:
The patent applies partial action by selectively correcting only those pixels that fall outside expected intensity ranges, rather than processing the entire image uniformly. The algorithm identifies specific problematic pixels and applies correction only to those regions, preserving processing speed while improving authentication reliability where it is most needed.
Data Source
AI summary
An image captured for a currency note is preprocessed to correct pixel intensity values to expected pixel intensity values. The corrected image is passed to a currency validator for determining whether the currency not is genuine or non-genuine.