Adaptive Noise Filter for Surgical Image Signal Stability
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Solution Overview
Problem
Existing image pipelines in computer-assisted surgical systems face challenges in effectively handling noise at varying signal levels, leading to issues such as noise-induced flicker in bright regions and loss of information in low signal level pixels due to the use of fixed noise filter parameters.
Innovation Solution
The implementation of adaptive noise filters that adjust noise filtering based on local signal levels, using both temporal and spatial noise filtering methods to differentiate between noise and real changes in pixel signal levels, thereby optimizing image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed noise filter parameters are used, then device complexity is reduced, but image quality deteriorates due to noise-induced flicker in bright regions and loss of information in low signal level pixels
Solution Approach 1:
The patent implements dynamic noise filtering by adjusting filter parameters based on local signal levels in the image. The system transitions from fixed parameters to adaptive parameters that change according to the signal characteristics, using a signal level map to guide parameter selection for different regions of the image.
Solution Approach 2:
The patent applies different noise filtering parameters to different local regions of the image based on their signal levels. Bright regions use one set of parameters while low signal regions use another, allowing each area to be optimized independently rather than applying a uniform approach across the entire image.
2Object-affected harmful factors
If aggressive noise filtering is applied, then noise-induced flicker is reduced, but information is lost in low signal level pixels
Solution Approach 1:
The system applies different filtering strengths to different regions by selecting parameters based on local signal levels. Low signal regions receive gentler filtering to preserve information, while bright regions with higher tolerance for filtering receive more aggressive noise reduction to eliminate flicker.
Solution Approach 2:
The patent changes filtering parameters dynamically based on signal level conditions. By monitoring the signal level map and adjusting filter parameters accordingly, the system adapts the filtering intensity to match the local image characteristics, preventing information loss in critical low-signal areas.
3Reliability
If adaptive noise filtering is implemented, then image quality is improved, but device complexity increases
Solution Approach 1:
The patent segments the image processing into distinct stages: generating a signal level map, selecting parameters based on signal levels, and applying region-specific filtering. This segmentation allows the complex adaptive filtering to be broken down into manageable steps that can be implemented systematically.
Solution Approach 2:
The signal level map serves as an intermediary that bridges the input image and the filtering process. This intermediate data structure enables the system to make informed parameter selection decisions without requiring complex real-time analysis during the filtering stage itself.
Data Source
AI summary
An example method includes determining an estimated pixel noise parameter based on a signal level of a first pixel of the first frame of pixel data; determining a difference between the signal level of the first pixel and at least a signal level of a second pixel; obtaining a filtered pixel based at least in part on a comparison of the difference to the estimated pixel noise parameter; and outputting the filtered pixel to an output frame of filtered pixel data.