Fine Line Restoration in Image Resolution Conversion
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Solution Overview
Problem
Existing image processing technologies face challenges in reducing image data size without compromising image quality, particularly in maintaining fine lines during resolution conversion, which can result in gaps and illegibility issues.
Innovation Solution
An image processing apparatus and program that detects fine lines, converts image data to a prescribed resolution, and restores detected fine lines to prevent gaps, using a fine line detection unit, resolution conversion unit, and restoration unit to ensure accurate preservation of fine lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If resolution conversion is performed to reduce data amount, then data compression ratio is improved, but fine line quality deteriorates with gaps appearing
Solution Approach 1:
The patent applies preliminary action by detecting fine lines in the original high-resolution image data before resolution conversion. This allows the system to identify which lines require special protection during the subsequent downsampling process, enabling targeted restoration after conversion to prevent gap formation in fine lines.
Solution Approach 2:
The patent introduces an intermediary mechanism by creating a separate fine line detection map that acts as a guide during restoration. This intermediary data structure allows the system to selectively restore only those pixels that belong to fine lines, rather than applying blanket restoration to the entire image, thus maintaining precision while managing complexity.
2Quantity of substance
If resolution conversion is performed to reduce data amount, then storage efficiency is improved, but image quality deteriorates
Solution Approach 1:
The patent applies local quality by differentiating between different regions of the image and applying different processing strategies. Fine line regions are detected and restored with higher priority using targeted algorithms, while other regions undergo standard resolution conversion. This localized approach preserves image quality in critical areas while maintaining overall data compression.
Solution Approach 2:
The patent changes parameters by adjusting restoration strength and threshold values based on the detected fine line characteristics. The restoration process modifies pixel values selectively based on detected fine line patterns, changing the local parameters of affected regions to eliminate gaps while preserving the overall resolution conversion benefits.
3Manufacturing precision
If fine line restoration is performed after resolution conversion, then fine line quality is improved, but processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the image processing into distinct stages: fine line detection before conversion, standard resolution conversion, and targeted fine line restoration after conversion. This segmentation allows each module to be optimized independently, reducing overall processing complexity while achieving high fine line quality through specialized restoration algorithms.
Solution Approach 2:
The patent applies partial action by performing restoration only on detected fine line regions rather than the entire image. The fine line detection map enables the system to apply restoration algorithms selectively to specific pixels or regions, reducing the computational burden compared to full-image restoration while maintaining fine line quality.
Data Source
AI summary
In order to prevent the appearance of gaps in fine lines when image data undergoes resolution conversion, the image processing apparatus includes an image reading unit that inputs image data, a fine line detection unit 113 that detects fine lines from the image data, a resolution conversion unit 111 that converts the image data to a prescribed resolution, and a fine line restoration unit 114 that restores fine lines in the image data that has undergone resolution conversion.