Parametric Color Blurring for Accurate Dental Simulations
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Solution Overview
Problem
Current techniques for generating simulated images of orthodontic treatment outcomes in dentistry often fail to produce accurate coloration of teeth, leading to unrealistic or discolored representations.
Innovation Solution
The use of global blurring techniques, specifically parametric functions like biquadratic functions, in conjunction with generative adversarial networks (GANs), to generate accurate color data for simulated images by creating a blurred color representation of teeth and gingiva, which is then combined with image data of post-treatment contours to produce realistic and accurate simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If local blurring methods are used to generate simulated tooth images, then the processing speed is improved, but the coloration accuracy deteriorates
Solution Approach 1:
The patent applies different blurring strategies to different regions of the image. Global blurring is applied to the entire tooth region to maintain color consistency, while local blurring is selectively applied only to specific areas where structural detail needs to be preserved. This regional differentiation resolves the contradiction by optimizing both speed and accuracy in appropriate zones.
Solution Approach 2:
The patent segments the tooth image into multiple regions with different blurring requirements. The segmentation allows the system to apply computationally efficient global blurring to large uniform areas while applying more intensive local blurring only where necessary, thereby maintaining both processing speed and coloration accuracy.
2Measurement precision
If global blurring techniques are used to generate simulated tooth images, then the coloration accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent changes the blurring parameters dynamically based on the specific requirements of different image regions. By adjusting blur radius, kernel size, and intensity parameters locally rather than applying a fixed global blur, the system achieves high coloration accuracy without requiring excessively complex processing across the entire image.
Solution Approach 2:
The patent applies global blurring selectively only to regions where color consistency is the primary concern, rather than to the entire image. This partial application of global blurring reduces overall processing complexity while maintaining coloration accuracy where it matters most.
3Use of energy by moving object
If traditional image generation techniques are used, then the computational resources required are reduced, but the realism of the simulated images deteriorates
Solution Approach 1:
The patent segments the image processing into distinct stages: initial global blurring for color foundation, followed by selective local blurring for detail enhancement. This segmentation allows the use of computationally efficient methods for the bulk of the processing while applying more resource-intensive techniques only where needed to achieve realism.
Solution Approach 2:
The patent performs global blurring as a preliminary step before applying local blurring or other processing. This preliminary action establishes the correct color foundation early, reducing the need for later corrective processing and thereby lowering overall computational resource requirements while maintaining realism.
Data Source
AI summary
A system includes a memory and a processing device operatively coupled to the memory. The processing device receives an image of a smile of an individual comprising initial contours of teeth of the individual, receives or generates image data comprising target contours of the teeth of an individual, and generates a new image based on the received image data and one or more parametric functions associated with tooth color determined from the image of the smile, wherein a shape of the teeth in the new image is based on the received image data and a color of the teeth in the new image is based on applying the one or more parametric functions to at least a portion of the image data.