Test Chart Image Mapping for Precise Print Density Correction
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Solution Overview
Problem
Existing methods for density unevenness correction in printing face challenges such as low detection accuracy of alignment marks, high computational load, and ink absorption leading to medium shrinkage, which affect the precision of density correction.
Innovation Solution
A method and apparatus for creating density unevenness correction data using a test chart with alignment marks and line marks, employing a rough and detailed correspondence relationship to enhance detection accuracy and account for medium expansion/shrinkage, with processing techniques like brightness enhancement and image filtering to improve alignment mark detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If alignment marks are used for position detection in the test pattern, then the density unevenness correction accuracy is improved, but the detection accuracy of alignment marks deteriorates due to ink absorption and medium shrinkage
Solution Approach 1:
The patent introduces line marks as intermediary elements between alignment marks and density patterns. These line marks serve as reference points that are less affected by ink absorption and medium shrinkage, enabling more accurate detection and calculation of deformation parameters while maintaining the correction accuracy benefit.
Solution Approach 2:
The patent changes the detection parameters by using multiple types of marks (alignment marks and line marks) with different characteristics. By detecting both alignment marks for positional information and line marks for deformation reference, the system compensates for the reduced accuracy of individual alignment mark detection through combined parameter analysis.
2Manufacturing precision
If two-dimensional deformation processing is applied to the scan image, then the density unevenness correction accuracy is improved, but the computational load increases
Solution Approach 1:
The patent segments the correction process into independent steps: first detecting alignment marks and line marks separately, then calculating deformation parameters based on line mark positions, and finally applying correction only to the density pattern regions. This segmentation reduces the overall computational complexity compared to processing the entire image as a whole.
Solution Approach 2:
The patent applies different processing qualities to different regions: high-precision deformation calculation is applied only to the density pattern areas using the deformation parameters derived from line marks, while alignment mark regions serve as reference without requiring full deformation processing. This local quality approach reduces computational load while maintaining accuracy where needed.
3Area of stationary object
If multiple scan images are combined for wide media, then the complete medium coverage is achieved, but the position detection accuracy deteriorates due to shared alignment marks
Solution Approach 1:
The patent segments the medium into multiple scan regions, each with its own alignment marks and line marks. By detecting line marks within each scanned region independently and using them as local references, the system achieves both complete coverage and accurate position detection without relying on shared alignment marks across regions.
Solution Approach 2:
The patent uses line marks as intermediary reference points within each scan region that are less affected by the shared alignment mark limitations. These local line marks enable accurate position detection within each region while the combination of multiple regions achieves the required wide medium coverage.
Data Source
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AI summary
Provided are a density unevenness correction data creation method, a density unevenness correction data creation apparatus, a printing system, a program, a test chart, and a test chart data creation apparatus in which density unevenness correction with high accuracy is implemented. A captured image of a test chart including a density step pattern, an alignment mark, and a line mark is acquired, and in a case of acquiring correspondence relationship information between a theoretical position and an imaging position from the captured image, a rough correspondence relationship is acquired by using positional information of the alignment mark, a detailed correspondence relationship is acquired by using positional information of the line mark estimated by using the rough correspondence relationship, and density unevenness correction data is created by estimating a density value of each position of the density step pattern by using the detailed correspondence relationship.