Aircraft Imaging Sensor ROI Gain Adjustment
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Solution Overview
Problem
Imaging systems on aircraft face challenges in maintaining consistent image quality due to rapid scene changes and dynamic luminance ranges, leading to degradation of user perception and potential loss of target objects, especially during tasks like aerial refueling.
Innovation Solution
A system that includes an imaging sensor and a processor to identify a reference region of interest with fixed characteristics, allowing for adaptive gain and offset adjustments based on these characteristics rather than the entire image, ensuring improved visibility of target objects by optimizing image intensity ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If adaptive gain adjustment is applied to the entire image, then overall image intensity is improved, but the region of interest may become blurred or obscured
Solution Approach 1:
The image is segmented into a region of interest (ROI) and a non-ROI region. The processor applies different gain values to these segments: a first gain value to the ROI to maintain its visibility and characteristics, and a second gain value to the non-ROI region to improve overall image intensity. This segmentation allows simultaneous optimization of both overall intensity and ROI visibility.
Solution Approach 2:
Different parts of the image are assigned different quality characteristics through selective gain adjustment. The ROI maintains its original intensity characteristics with a conservative gain value, while the non-ROI region receives aggressive gain adjustment to enhance overall image brightness. This local quality approach ensures that the ROI remains unblurred while the rest of the image benefits from intensity enhancement.
2Illumination intensity
If gain value is increased to improve image intensity range, then visibility of dark regions is improved, but bright regions may become overexposed
Solution Approach 1:
The system dynamically adjusts the gain value parameter based on the detected intensity characteristics of the ROI. When the ROI intensity range falls below a threshold, a higher gain value is applied to boost intensity; when it exceeds the threshold, a lower gain value is used to prevent overexposure. This parameter adaptation ensures optimal intensity range expansion without causing harmful overexposure effects.
3Measurement precision
If manual adjustment of imaging parameters is allowed, then image quality can be optimized, but operator workload and response time increase
Solution Approach 1:
The imaging system performs self-adjustment by automatically detecting the intensity characteristics of the ROI and computing the appropriate gain value without operator intervention. The processor continuously monitors ROI intensity and autonomously adjusts the gain parameter to maintain optimal visibility, thereby eliminating the need for manual parameter adjustment and reducing operator workload and response time.
Solution Approach 2:
The system implements a feedback loop where the processor continuously monitors the intensity range of the ROI and uses this information to dynamically adjust the gain value. This closed-loop feedback mechanism ensures that image quality is automatically optimized in real-time based on actual ROI characteristics, maintaining high measurement precision without requiring continuous manual intervention.
Data Source
AI summary
Systems and methods for improving target object visibility in captured images can include an imaging sensor for mounting on an aircraft at a fixed position and orientation. A predetermined portion of the aircraft can appear as a region of interest (ROI) at a predefined location across images captured by the imaging sensor. A processor can receive a first image signal captured by the imaging sensor and corresponding to a first image that includes the ROI. The processor can determine a first image intensity range of the ROI in the first image and gain and offset values for modifying the first image or a subsequent image. The processor can cause a second image signal, received from the imaging sensor, to be amplified according to the gain and offset values and cause the region of interest in a second image to have a second image intensity range different from the first image intensity range.