Pattern Recognition Selective Noise Reduction 3D Depth Maps
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Solution Overview
Problem
In computerized systems using laser-based 3D depth mapping, noise reduction algorithms can blur image edges when applied to low signal-to-noise ratio depth information, particularly affecting detailed features like eyes and eyelashes, due to insufficient laser illumination.
Innovation Solution
Implementing pattern recognition to selectively apply noise reduction or modulate its strength based on object recognition, allowing different noise reduction levels for various objects within an image to preserve sharpness and detail.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noise reduction is applied to depth map images with low signal-to-noise ratio, then noise is reduced, but image edges become blurred and sharpness is reduced
Solution Approach 1:
The patent applies different noise reduction levels to different regions of the image based on object identification. Skin regions receive higher noise reduction levels while eye regions receive lower or zero noise reduction levels, allowing each region to be processed according to its specific requirements rather than applying a uniform approach to the entire image
2Ease of operation
If uniform noise reduction is applied across the entire image, then processing is simple, but detailed features like eyes and eyelashes are blurred
Solution Approach 1:
The patent segments the image into different object regions (skin, eyes, eyelashes, eyebrows) using pattern recognition and object identification. Each segmented region is then assigned an appropriate noise reduction level from a data structure, enabling differentiated processing that preserves critical details while still reducing noise in appropriate areas
Data Source
AI summary
To reduce the random noise in a depth map that is rendered using colors to convey the various depths, pattern recognition may be used to selectively apply noise reduction or to modulate the strength of the noise reduction. In this way, the potential adverse effect on the detail/sharpness of the image can be ameliorated. For example, in an image of a person, the skin does not have any sharp edges so noise reduction can be applied to such an image with little adverse consequence, whereas noise reduction applied to the image of a person's eye can cause loss of the detail of the iris, eyelashes, etc. Using pattern recognition on objects in the image, the appropriate level of noise reduction can be applied across an image while minimizing blurring/loss of detail.