Edge Enhancement Algorithm for Low-Light Noise Control
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Solution Overview
Problem
Digital imaging systems face challenges in enhancing image edges in light deficient environments due to the increase of noise with signal levels, leading to degraded image quality and unnatural image appearance.
Innovation Solution
An edge enhancement algorithm that continuously varies the threshold pixel by pixel based on knowledge of expected local noise, using signal calibration to predict noise levels and apply optimal edge enhancement, employing methods like the unsharp mask or Canny method to separate true edges from noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If edge enhancement is applied to improve image quality in low-light conditions, then image clarity and edge definition are improved, but noise is amplified and image quality degrades
Solution Approach 1:
The patent applies different edge enhancement thresholds to different regions of the image based on local noise characteristics. The processing circuitry analyzes local signal levels and noise expectations in each region, then applies customized enhancement parameters to each local area rather than using a global threshold, thereby improving edge detection accuracy while controlling noise amplification in each specific region
Solution Approach 2:
The patent dynamically adjusts edge enhancement parameters (thresholds, gain factors) based on local signal calibration and noise level predictions. The system continuously varies these parameters pixel by pixel according to the expected local noise characteristics, allowing optimal edge enhancement to be applied adaptively while controlling noise amplification in different image regions
2Ease of operation
If fixed threshold edge enhancement is used, then processing is simple and fast, but it cannot adapt to varying noise levels across different image regions
Solution Approach 1:
The patent transforms the static fixed-threshold approach into a dynamic system where edge enhancement parameters are continuously adjusted based on local signal characteristics and predicted noise levels. The processing circuitry dynamically calculates and applies different thresholds to different pixels or regions, enabling the system to adapt to varying noise conditions while maintaining computational efficiency through localized processing
Data Source
AI summary
The disclosure extends to methods, systems, and computer program products for enhancing edges within an image in a light deficient environment, which utilizes knowledge of the expected noise pixel by pixel, to control the strength of the edge enhancement and thereby limit the impact of the enhancement on the perception of noise.