Depth-Based Gain Control for Imaging Contrast
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Solution Overview
Problem
Existing image-based depth mapping systems fail to achieve optimal contrast in images due to the depth dependence of pattern brightness, as conventional automatic gain control methods do not account for the depth of objects, leading to either saturation in bright areas or noise in dim areas.
Innovation Solution
The system captures electronic images of a scene, processes them to generate depth data, and adjusts the imaging device's gain based on the depth data, using pre-calibrated gain values derived from multiple images of a calibration object at different distances, ensuring optimal contrast by avoiding saturation and enhancing pattern visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional automatic gain control is used, then the imaging system can operate with simple gain adjustment, but the image quality deteriorates due to saturation in bright areas and noise in dim areas caused by depth-dependent pattern brightness
Solution Approach 1:
The system uses depth information as feedback to dynamically adjust the gain setting. The processor determines depth values for pixels and uses these depth values to select appropriate gain values, creating a closed-loop control system that adapts to varying scene depths and maintains optimal image quality across different depth ranges.
Solution Approach 2:
The gain control transitions from a static conventional AGC approach to a dynamic depth-dependent gain adjustment. The system continuously adapts gain settings based on real-time depth information, allowing the imaging system to respond to changing scene conditions and maintain optimal contrast across varying depths.
2Measurement precision
If depth-dependent gain adjustment is implemented, then image contrast is improved across varying depths, but the system complexity increases due to additional depth processing requirements
Solution Approach 1:
The system merges the depth mapping function with the gain control function into an integrated processing pipeline. By combining these two functions, the system avoids the need for separate independent processing modules and achieves depth-dependent gain adjustment without proportionally increasing overall system complexity.
Solution Approach 2:
The system changes the gain parameter based on depth information. Instead of using a fixed or simply adaptive gain, the gain value becomes a function of depth, allowing the system to optimize image quality for objects at different distances while maintaining a relatively simple processing architecture.
3Productivity
If uniform gain is applied across the entire image, then the processing is simple and fast, but the contrast is suboptimal for objects at different depths due to varying pattern brightness
Solution Approach 1:
The system applies different gain values to different regions of the image based on their depth characteristics. Instead of using a single uniform gain for the entire image, the gain is localized to specific depth ranges or regions, ensuring that each area receives the appropriate gain setting for optimal pattern visibility and contrast.
Data Source
AI summary
A method for depth mapping includes capturing an electronic image of a scene using an imaging device. The electronic image is processed to generate depth data with respect to the scene. The gain of the imaging device is set responsively to the depth data.