Image Processing Method for Inkjet Nozzle Defect Correction
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Solution Overview
Problem
Existing image processing methods for ink jet printing face challenges in suppressing artifacts at boundaries between threshold value matrices, particularly when defective nozzles occur in a concentrated manner, leading to poor image quality and incomplete correction of streaks.
Innovation Solution
An image processing method that uses a combination of threshold value matrices for normal, correction, and boundary regions, where the third threshold value matrix is applied to the boundary region to suppress artifacts, and the second threshold value matrix is used for regions with closely spaced defective nozzles, ensuring high-quality correction without granularity deterioration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple threshold value matrices are prepared for different non-jetting patterns, then defective nozzle correction capability is improved, but stored capacity increases significantly
Solution Approach 1:
The patent segments the image region into multiple zones based on non-jetting patterns, and applies different threshold value matrices to different zones. Instead of storing complete threshold value matrices for all possible non-jetting patterns, the patent stores only the necessary portions (segments) of threshold value matrices corresponding to each zone type, significantly reducing storage requirements while maintaining correction capability for various defective nozzle patterns
Solution Approach 2:
The patent applies local quality by using different threshold value matrices for different regions of the image based on the non-jetting pattern. Each region receives a threshold value matrix optimized for its specific characteristics, allowing high-quality correction locally while avoiding the need to store complete sets of matrices for all possible patterns globally
2Quantity of substance
If threshold value matrix size is decreased to reduce stored capacity, then storage requirements are reduced, but blue noise mask cannot be used and high image quality cannot be obtained
Solution Approach 1:
The patent segments the threshold value matrix into multiple smaller sub-matrices, each stored separately. This segmentation allows the system to use compact blue noise masks for each sub-matrix while maintaining overall high image quality through proper combination of the sub-matrices during processing
Solution Approach 2:
The patent transitions from storing complete two-dimensional threshold value matrices to storing one-dimensional arrays of zone definitions and smaller sub-matrices. This dimensional change in data organization allows efficient storage of blue noise masks while preserving image quality through systematic reconstruction during processing
3Quantity of substance
If sub-matrix size is decreased to handle multiple non-jetting occurrences, then storage capacity is reduced, but artifact suppression at boundaries becomes insufficient
Solution Approach 1:
The patent introduces an intermediary processing step that detects boundary regions between different non-jetting zones and applies special artifact suppression techniques at these boundaries. This intermediary mechanism coordinates the interaction between adjacent sub-matrices, preventing artifacts while maintaining efficient storage of smaller sub-matrices
4Object-affected harmful factors
If sub-matrix size is increased to improve boundary artifact suppression, then artifact suppression capability is improved, but storage capacity increases and correction quality deteriorates when multiple non-jetting nozzles occur
Solution Approach 1:
The patent segments the image into zones with different non-jetting patterns and applies appropriately-sized sub-matrices to each segment. This segmentation allows the system to use larger sub-matrices only where boundary artifact suppression is critical, while using smaller sub-matrices in other regions, optimizing the trade-off between artifact suppression and storage capacity
Solution Approach 2:
The patent applies local quality by using different sub-matrix sizes for different regions based on the non-jetting pattern complexity. Regions with simple patterns use smaller sub-matrices for efficient storage, while regions with complex patterns or boundaries use larger sub-matrices for better artifact suppression, achieving optimal performance locally throughout the image
Data Source
AI summary
An image processing method, an apparatus, a program, and an image forming apparatus capable of suppressing an artifact in a boundary part of a threshold value matrix and performing high-quality correction even in a case where defective recording elements are concentrated are provided. An image processing method according to an aspect of the present invention includes performing correction of suppressing visibility of an image defect using recording elements around a defective recording element of a recording head by disabling the defective recording element, and performing quantization of data of an image, in which processing of the quantization includes applying a first threshold value matrix to a first image region that is separated by more than a first distance from a defective image region, applying a second threshold value matrix to a second image region separated by less than the first distance, and applying a third threshold value matrix to a third image region at a boundary between the first image region and the second image region.