Imaging System Phase Modulation Nonlinear Processing
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Solution Overview
Problem
Conventional imaging systems face challenges in efficiently processing images to enhance quality, particularly in machine vision applications where image quality is critical for task performance, due to limitations in computational resources and the subjective nature of human perception.
Innovation Solution
The implementation of an imaging system with phase modifying elements that introduce specific image attributes into optical images, followed by a signal processor that subdivides and independently processes electronic data sets to maintain and adjust these attributes, enabling nonlinear and spatially varying processing to improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used to enhance image quality, then image quality improves, but computational resources (memory space and computing time) increase significantly
Solution Approach 1:
The image data is divided into multiple data sets based on image attributes such as spatial frequency, contrast, or region of interest. Each data set is processed independently with optimized algorithms tailored to its specific characteristics, reducing overall computational burden while maintaining image quality enhancement
Solution Approach 2:
Different processing strategies are applied to different regions or frequency components of the image based on their specific requirements. High-frequency regions may receive different processing than low-frequency regions, optimizing resource allocation according to local image characteristics
2Reliability
If advanced image processing algorithms are applied to improve image quality for machine vision tasks, then task performance improves, but processing time increases
Solution Approach 1:
Image data is pre-processed and organized into meaningful data sets before the main processing stage. Attributes are extracted and used to guide subsequent processing, reducing the complexity of the main algorithm and accelerating processing time while maintaining task performance
Solution Approach 2:
Processing parameters such as filter sizes, threshold values, or algorithm complexity are dynamically adjusted based on image characteristics and task requirements. This allows optimization of processing speed for different types of images and tasks while maintaining sufficient image quality
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
This approach enhances image quality by effectively addressing computational resource constraints and subjective/objective factors, improving image usability in both human perception and machine vision tasks, such as iris recognition and package sorting systems.
Implementation Method 1
optics having one or more phase modifying elements that modify wavefront phase to introduce image attributes into an optical image
Implementation Method 2
A detector converts the optical image to electronic data while maintaining the image attributes
Data Source
AI summary
A method, associated systems, and apparatus are described for imaging a scene to produce electronic image data representing the scene and having at least one improved characteristic. Encoding optics image and encode electromagnetic energy radiated or reflected from a scene, including its objects and features, and a detector receives the encoded electromagnetic energy to produce electronic image data which is at least partially decoded by a pre-processing operation to produce pre-processed electronic image data, having at least one improved characteristic as a result of cooperation between the encoding and decoding. A subset of the pre-processed image data may include artifacts as a result of the encoding and decoding; the artifacts are further processed by applying a nonlinear post-processing operation that identifies the artifacts in the pre-processed imaged data and compensates the associated image data values while retaining the improved image characteristics in the balance of the pre-processed image data.