CNN Color Space Conversion for Manufactured Surface Printing
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Solution Overview
Problem
Existing methods for image quality enhancement and color space conversion in manufactured surfaces printing are inefficient and require manual, complex procedures, failing to accurately reproduce the full range of colors in high-resolution RGB scans due to lossy conversions.
Innovation Solution
A computer-implemented system using convolutional neural networks, particularly U-Net, automates the enhancement and conversion process by approximating conversion and enhancement functions, enabling precise color reproduction on manufactured surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual enhancement procedures are used in Photoshop, then color properties can be preserved and enhanced, but the process becomes complex and time-consuming
Solution Approach 1:
The patent replaces manual mechanical enhancement operations in Photoshop with an automated deep learning system. The neural network model automatically performs color enhancement and optimization without requiring manual designer intervention, thus preserving color properties while eliminating time-consuming manual procedures.
Solution Approach 2:
The enhancement system performs self-service by automatically analyzing and optimizing image color properties without external human intervention. The neural network independently completes the enhancement task that previously required skilled designers to manually simulate actual rendering and adjust color properties.
2Adaptability or versatility
If conversion to multichannel spaces is performed, then color management for manufactured surfaces is achieved, but color range reproduction becomes lossy
Solution Approach 1:
The patent applies preliminary action by performing enhancement in the source RGB color space before conversion to multichannel space. The neural network optimizes the image in the full-color RGB space where all color information is preserved, then converts to the target multichannel space. This prevents information loss that would occur if enhancement were attempted after lossy conversion.
3Productivity
If automated conversion is used, then processing speed increases, but enhancement quality decreases compared to manual procedures
Solution Approach 1:
The patent substitutes manual mechanical enhancement operations with an automated neural network system that achieves both speed and quality. The deep learning model is trained to replicate and enhance the quality of manual designer work while operating automatically, thus maintaining high enhancement quality while dramatically increasing processing speed.
4Extent of automation
If semi-automatic tools are used, then some automation is achieved, but complex procedures still require manual intervention
Solution Approach 1:
The patent merges the conversion and enhancement operations into a single unified neural network process. Instead of separate semi-automatic tools that require manual intervention at multiple stages, the system combines color space conversion and quality enhancement into one automated workflow, reducing overall procedure complexity while maintaining full automation.
Data Source
AI summary
A computer-implemented system for image quality enhancement and color spaces conversion for manufactured surfaces printers comprises at least a convolutional neural network, configured for executing at least the following steps: receive in input an original image x′ in RGB space; from the original image x′, elaborate an approximation {tilde over (x)}+″ in multichannel space or in RGB space of an enhanced image x+″ for manufactured surfaces printers; and return to output the approximation {tilde over (x)}+″ of the enhanced image x+″ for the use in manufactured surfaces printers.