Ophthalmic Microscope Exposure Control via Edge Density Weighting
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Solution Overview
Problem
Existing ophthalmic microscope devices struggle to achieve improved image quality due to challenges in exposure control, particularly in focusing on regions of interest while managing out-of-focus areas.
Innovation Solution
The method involves recording a camera image, using edge detection to identify sub-regions with varying edge densities, calculating a brightness parameter by weighing pixel brightness in edge-rich sub-regions more strongly, and adjusting exposure parameters accordingly to optimize image exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic exposure control is used to adjust exposure parameters based on average brightness, then exposure correction is implemented, but image quality deteriorates due to underexposure or overexposure of out-of-focus regions
Solution Approach 1:
The patent applies local quality by differentiating between in-focus and out-of-focus regions through edge density analysis. Regions with high edge density (in-focus) are weighted more heavily in brightness calculation, while uniform regions (out-of-focus) are weighted less. This selective weighting ensures exposure parameters are optimized for the region of interest rather than being averaged across the entire image, thereby improving image quality while maintaining reliable exposure control.
2Illumination intensity
If exposure parameters are adjusted to illuminate the entire scene, then all regions receive light, but the region of interest does not achieve optimal exposure due to small depth of field
Solution Approach 1:
The patent implements local quality by calculating brightness parameters selectively for different image regions based on edge density. The region of interest (high edge density) receives prioritized exposure optimization, while out-of-focus regions (low edge density) are given less weight. This allows the system to concentrate illumination resources on the critical region rather than uniformly distributing light, achieving optimal exposure for the region of interest despite limited depth of field.
3Device complexity
If average brightness calculation is used for exposure correction, then simple computation is achieved, but exposure accuracy deteriorates due to influence of out-of-focus regions
Solution Approach 1:
The patent resolves this contradiction by introducing edge density-based regional weighting that maintains computational efficiency while improving brightness measurement accuracy. Instead of uniformly averaging all pixels, the system divides the image into regions based on edge density characteristics and applies differential weighting. This approach selectively emphasizes in-focus regions in the brightness calculation, achieving accurate exposure measurement without requiring complex processing algorithms.
Solution Approach 2:
The patent applies preliminary action by performing edge detection and region classification before the exposure calculation step. By pre-identifying high-edge-density regions (in-focus areas) and low-edge-density regions (out-of-focus areas), the system prepares the spatial weighting map in advance. This preliminary segmentation allows the subsequent brightness calculation to efficiently apply regional weights without adding significant computational overhead, maintaining simplicity while improving accuracy.
Data Source
AI summary
Image exposure in an ophthalmic microscope device is controlled by first recording a camera image with the camera of the microscope using first exposure parameters. Edge detection, e.g., based on convolution using a discrete differential operator, is then used to identify regions where the image has high and low edge densities. A brightness parameter of the camera image is calculated by weighing the pixel brightness in the regions with increased edge density more strongly than in the other regions. The brightness parameter is then used to control the exposure parameters of the microscope.