Depth Map Resolution via False Pixel Confidence Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing depth map technologies face challenges in increasing resolution without compromising image quality, particularly due to the trade-off between noise reduction and image resolution in time of flight techniques, where larger pixel sizes reduce noise but decrease resolution.
Innovation Solution
The method involves assigning confidence values to depth pixels based on curvature and localized contrast information, identifying false depth pixels, and updating their values through extrapolation from neighboring pixels, thereby enhancing the resolution of depth maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If larger pixel sizes are used in time of flight techniques, then noise is reduced, but image resolution decreases
Solution Approach 1:
The patent divides the depth map processing into multiple stages: initial depth map generation, false pixel identification, and selective updating. By segmenting the problem into identifying false depth pixels versus true depth pixels, the system can apply different processing strategies to maintain resolution while reducing noise through confidence-based filtering.
Solution Approach 2:
The patent applies local quality by treating different pixels differently based on their confidence values. False depth pixels identified through curvature and contrast analysis receive updating treatment, while high-confidence pixels are preserved. This localized approach allows noise reduction in specific regions without compromising overall image resolution.
2Manufacturing precision
If depth map resolution is increased, then image quality improves, but noise increases
Solution Approach 1:
The patent implements feedback through confidence value calculation and comparison. By computing confidence values based on curvature and localized contrast, then using these values to identify and update false pixels, the system creates a feedback loop that continuously refines the depth map quality while maintaining resolution.
Solution Approach 2:
The patent changes parameters by introducing confidence values as an additional parameter to distinguish true from false depth pixels. This parameter change enables the system to process high-resolution depth maps while filtering out noise through confidence-based identification and selective updating of depth values.
3Manufacturing precision
If false depth pixels are corrected through extrapolation, then image quality improves, but processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-identifying false depth pixels using curvature and localized contrast analysis before performing extrapolation. This preliminary identification step allows the system to target only problematic pixels for updating, reducing the overall processing complexity compared to processing the entire depth map.
Solution Approach 2:
The patent uses partial action by applying updating only to identified false depth pixels rather than processing the entire depth map. This selective approach improves image quality where needed while minimizing processing complexity by avoiding unnecessary computations on already accurate pixels.
Data Source
AI summary
Systems and methods for increasing the resolution of a depth map by identifying and updating false depth pixels are described. In some embodiments, a depth pixel of the depth map is initially assigned a confidence value based on curvature values and localized contrast information. The curvature values may be generated by applying a Laplacian filter or other edge detection filter to the depth pixel and its neighboring pixels. The localized contrast information may be generated by determining a difference between the maximum and minimum depth values associated with the depth pixel and its neighboring pixels. A false depth pixel may be identified by comparing a confidence value associated with the false depth pixel with a particular threshold. The false depth pixel may be updated by assigning a new depth value based on an extrapolation of depth values associated with neighboring pixel locations.