Adaptive Noise Averaging for Handheld Thermal Imaging Cameras
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Solution Overview
Problem
Handheld thermal imaging cameras face challenges in noise optimization, particularly in capturing images with movement characteristics, where existing methods fail to effectively reduce noise based on camera movement, leading to suboptimal image quality.
Innovation Solution
A method where a computational unit in the camera determines the number of images to be averaged based on the intensity of movement characteristics, such as spatial position, speed, or temperature changes, to adaptively reduce noise, and adjusts the image repetition rate accordingly, utilizing a combination of sensors like infrared, light, temperature, and position sensors for real-time data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If averaging of image data is performed to reduce noise, then image quality is improved, but computational complexity increases
Solution Approach 1:
The patent applies dynamics by making the number of images to be averaged variable rather than fixed. The computational unit dynamically adjusts the averaging count based on real-time movement characteristics detected by sensors. When camera movement is detected, the system reduces the number of images averaged; when the camera is stable, it increases the averaging count. This dynamic adaptation resolves the contradiction by optimizing the balance between noise reduction and computational load according to actual operating conditions.
Solution Approach 2:
The patent changes the parameter of averaging count from a static value to a variable determined by movement characteristics. The computational unit modifies this parameter based on sensor data, adjusting the degree of noise reduction applied. This parameter change allows the system to achieve high image quality when needed while reducing computational complexity when movement is detected, effectively resolving the technical contradiction.
2Measurement precision
If the number of images to be averaged is increased to reduce noise, then image quality is improved, but image capture rate decreases
Solution Approach 1:
The system dynamically adjusts the averaging count based on camera movement characteristics. When movement is detected through sensors, the system reduces the number of images averaged, thereby maintaining a high image capture rate. When the camera is stable, it increases averaging to improve image quality. This dynamic adjustment resolves the contradiction between noise reduction and capture rate by adapting to real-time conditions.
Solution Approach 2:
The patent changes the averaging count parameter from a fixed high value to a variable that responds to movement characteristics. This allows the system to maintain high capture rates during movement while achieving noise reduction during stable periods, effectively resolving the contradiction between image quality and productivity.
3Measurement precision
If fixed noise reduction is applied regardless of camera movement, then noise is reduced, but image quality deteriorates during movement
Solution Approach 1:
The patent implements dynamics by making the noise reduction process adaptive to camera movement. The computational unit continuously monitors movement characteristics through sensors and adjusts the averaging count accordingly. During movement, the system reduces averaging to avoid degrading image quality; during stability, it increases averaging for optimal noise reduction. This dynamic adaptation resolves the contradiction between noise reduction and movement adaptability.
Solution Approach 2:
The system employs feedback by using sensor data about camera movement to control the noise reduction process. The computational unit receives feedback from sensors regarding movement characteristics and adjusts the averaging count in response. This closed-loop control ensures the system adapts to movement conditions, resolving the contradiction between applying noise reduction and maintaining image quality during movement.
Data Source
AI summary
The disclosure relates to a method for the noise optimization of a camera, in particular a handheld thermal imaging camera. Images are captured by means of the camera in at least one method step; at least one movement characteristic variable is detected by means of at least one sensor unit of the camera in at least one method step; and image data of captured images is averaged by means of a computing unit of the camera in at least one method step. At least a number of images to be averaged are determined by means of the computing unit of the camera at least on the basis of an intensity of the detected movement characteristic variable, in particular a change rate, in at least one method step.