Artificial Vision System Wavelet Enhancement for Brownout Navigation
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Solution Overview
Problem
Artificial vision systems struggle to provide clear visual information to pilots in degraded visual environments, such as brownout conditions, where sand obscures vision, leading to potential helicopter crashes due to obscured visibility.
Innovation Solution
An artificial vision system that captures sequential frames of infrared image data, normalizes each pixel, decomposes the data into wavelet frequency bands, and converts these bands into visible color images using a wavelet enhancement component and video processor, simulating atmospheric attenuation and noise suppression to display situational awareness data on a video display system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If infrared image data is captured and processed through wavelet decomposition and conversion, then visible color images are produced with enhanced clarity and situational awareness, but the device complexity increases due to multiple processing components
Solution Approach 1:
The image processing system is segmented into distinct functional modules: wavelet enhancement component for frequency decomposition, video processor for color space conversion, and display system for visualization. This modular segmentation allows each component to specialize in specific processing tasks, improving overall system reliability while managing complexity through organized functional division.
Solution Approach 2:
The wavelet enhancement component acts as an intermediary between the infrared sensor and the display system, transforming monochromatic infrared data into frequency bands that can be converted to visible color images. This intermediary processing step enables reliable navigation information to be extracted from infrared data while maintaining manageable system complexity through staged transformation.
2Measurement precision
If wavelet enhancement and frequency band decomposition are applied to infrared image data, then image clarity and noise reduction are improved, but the processing time and computational requirements increase
Solution Approach 1:
The wavelet enhancement component performs preliminary decomposition of infrared image data into frequency bands before conversion to visible images. By pre-processing the data into organized frequency components, the system establishes a structured foundation that reduces subsequent processing complexity and time requirements while maintaining high measurement precision.
Solution Approach 2:
The image processing system dynamically adjusts processing parameters based on the characteristics of the infrared input data. The wavelet decomposition adapts to varying scene conditions, optimizing the balance between noise reduction precision and processing speed according to the specific visual environment and degradation conditions detected.
Data Source
AI summary
One aspect of the present invention includes artificial vision system. The system includes an image system comprising a video source that is configured to capture sequential frames of image data of non-visible light and at least one processor configured as an image processing system. The image processing system includes a wavelet enhancement component configured to normalize each pixel of each of the sequential frames of image data and to decompose the normalized image data into a plurality of wavelet frequency bands. The image processing system also includes a video processor configured to convert the plurality of wavelet frequency bands in the sequential frames into respective visible color images. The system also includes a video display system configured to display the visible color images.