Barcode Decoding Using Super-Resolution Signal Reconstruction
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Solution Overview
Problem
Barcode readers face difficulties in decoding under-resolved barcodes due to low sampling rates or poor focus, leading to challenges in accurately measuring element widths and distinguishing between narrow and wide bars, especially at low resolutions.
Innovation Solution
A method is developed to decode under-resolved barcodes by modeling the local reflectance using techniques such as integrating the area under the actual barcode reflectance profile within scan sample bins or weighted summation, allowing for the determination of element widths based on sampling coefficients and scan signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If barcode readers use low sampling rates or low resolution sensors, then reading speed and cost are improved, but measurement precision of element widths deteriorates
Solution Approach 1:
The patent changes the parameters of the sampling process by using super-resolution algorithms that process low-resolution scan signals to reconstruct high-resolution barcode element width measurements. The system transforms the parameter space from direct pixel measurement to algorithmic reconstruction, allowing accurate element width determination even when sampling rate is low
Solution Approach 2:
The patent replaces the mechanical/optical resolution requirement with a computational approach. Instead of relying on high-resolution sensors or fast scanning mechanics, the system uses signal processing and mathematical models to achieve precise measurements from low-quality input data
2Device complexity
If barcode readers use low sampling rates, then device complexity and cost are reduced, but reliability of decoding under-resolved barcodes deteriorates
Solution Approach 1:
The patent introduces an intermediary computational layer between the low-resolution sensor and the barcode decoding process. Super-resolution algorithms act as a mediator that processes the raw scan signal and produces enhanced measurements, allowing reliable decoding without requiring complex high-resolution hardware
Solution Approach 2:
The patent creates a computational copy or reconstruction of the high-resolution barcode signal from low-resolution samples. By generating a super-resolved representation of the scan signal, the system achieves reliable decoding measurements without needing the actual high-resolution data
3Measurement precision
If barcodes are printed with bars placed relatively distant from other printing structures to differentiate from background, then readability is improved, but the space required increases
Solution Approach 1:
The patent changes the interpretive parameters of the barcode scan signal using super-resolution algorithms. By transforming the measurement scale and signal processing approach, the system can accurately distinguish barcode elements even when they occupy smaller spatial areas, reducing the required quiet zone and overall barcode space
Data Source
AI summary
Systems and methods are provided for decoding barcodes. A scan signal is acquired along a scan through a barcode. A first character unit grid for a unit width pattern within the barcode along the scan is determined. At least one set of sampling coefficients relating the unit width pattern to a portion of the scan signal is determined based on the first character unit grid. The element width pattern for the unit width pattern is determined based on the at least one set of sampling coefficients and the portion of the scan signal.