Localized Lens Focus Calibration for Computational Camera Yield
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Solution Overview
Problem
The manufacturing process of computational cameras faces challenges in achieving high yields and consistent image quality due to issues such as noise on data lines, lens imperfections, and inconsistencies in optical resolution across the lens field of view.
Innovation Solution
The proposed solution involves a method and system that simultaneously align the camera's optical center with its image sensor center, derive Modulation Transfer Function (MTF) requirements, and focus the camera, while iteratively evaluating other intrinsic parameters. This approach uses localized calibration parameters and omni-symmetrical regions within the lens field of view to improve focus and image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manufacturers improve lens image quality and manufacturing process, then measurement resolution is improved, but manufacturing complexity and cost increase
Solution Approach 1:
The patent performs focus parameter estimation and camera calibration during the manufacturing process itself, rather than requiring post-manufacturing adjustments. By localizing these operations to the assembly line, the system preliminarily establishes optimal optical parameters before the camera is deployed, resolving the contradiction by embedding quality assurance within the manufacturing flow rather than adding separate complex adjustment mechanisms
Solution Approach 2:
The system uses the camera's own imaging capabilities to evaluate and optimize its own focus parameters during manufacturing. By capturing images of a calibration target and computing focus metrics autonomously, the camera module self-calibrates without requiring external specialized equipment or manual intervention, thereby improving measurement resolution while avoiding additional manufacturing complexity
2Measurement precision
If more pixels are added to improve image quality, then image quality is improved, but signal to noise ratio and other quality metrics deteriorate
Solution Approach 1:
The patent optimizes focus parameters (such as lens-to-sensor distance and focal length) to maximize image quality for a given pixel configuration. By precisely controlling optical parameters during manufacturing rather than relying solely on increasing pixel count, the system achieves high image quality while maintaining favorable signal-to-noise characteristics, as optimal focus ensures maximum light efficiency and signal strength per pixel
3Productivity
If comprehensive assembly line processes are implemented to reduce defects, then manufacturing yield is improved, but manufacturing complexity increases
Solution Approach 1:
The patent replaces mechanical adjustment mechanisms with computational methods for focus optimization. Instead of using complex mechanical focus adjustment devices or multiple manual calibration steps, the system uses image processing algorithms and computational focus metrics to automatically determine and set optimal focus parameters, thereby improving yield through automation while reducing mechanical complexity
Solution Approach 2:
The calibration target and imaging system serve multiple functions: they are used for focus parameter estimation, intrinsic parameter calibration, and quality assessment all in one integrated process. This multi-functionality eliminates the need for separate testing and adjustment equipment at different stages of the assembly line, improving manufacturing yield through comprehensive quality control while reducing overall assembly line complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution effectively improves the accuracy and consistency of computational camera manufacturing by addressing lens alignment, MTF requirements, and intrinsic parameter evaluation, leading to enhanced image quality and increased manufacturing yields.
Implementation Method 1
A lens 230, an image sensor 220
Implementation Method 2
having a focused camera is critical for good image quality
Data Source
AI summary
A method and system for calibrating a lens. The method includes defining a plurality of omni-symmetrical regions within the lens, determining one or more localized lens parameters associated with each of the plurality of omni-symmetrical regions, and defining a localized set of calibration parameters for each of the plurality of omni-symmetrical region. The localized set of calibration parameters may then be employed in a computational image application.