Masked Compressive Imaging for Low-Data Image Capture
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Solution Overview
Problem
Traditional image sensors produce large amounts of data, which can be cumbersome and wasteful, as they capture and process entire images before reducing them mathematically, consuming resources that could be used more efficiently.
Innovation Solution
A compressive-sensing image capture device that uses an array of sensors with passive or dynamic masks to block electromagnetic radiation, effectively performing mathematical filtering in the physical world, reducing the need for processing and power consumption by capturing only necessary data, and employing a machine-trained network for image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image sensors capture entire images before reducing them mathematically, then complete image data is obtained, but processing resources and power consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by performing compression during the image capture process itself rather than after capture. The mask array blocks specific portions of light before the sensors detect the image, pre-filtering the data at the physical level. This preliminary compression action reduces the total data volume that needs subsequent mathematical processing, thereby lowering power consumption while maintaining essential image information
Solution Approach 2:
The patent extracts only the necessary image data by using mask arrays to block specific portions of light during capture. Instead of capturing complete high-resolution images and then removing unnecessary data through mathematical reduction, the system extracts only the required information at the capture stage. This extraction principle reduces processing burden and power consumption by eliminating redundant data before it enters the processing pipeline
2Loss of information
If traditional image sensors capture entire images, then all image information is preserved, but data volume becomes cumbersome and wasteful
Solution Approach 1:
The patent extracts only the necessary image information by using mask arrays to selectively block light during capture. The masks are designed to preserve essential image data while blocking redundant or less important portions. This extraction approach maintains the minimum required information for useful image processing while dramatically reducing the total data volume generated, eliminating the waste of storing and transmitting unnecessary pixels
Solution Approach 2:
The patent performs information filtering in advance during the capture process itself. The mask arrays are configured beforehand to block specific spatial frequencies or image regions that are less critical for the intended application. This preliminary action ensures that only necessary information is captured from the start, preventing the generation of cumbersome data volumes while preserving essential image content
3Productivity
If mathematical reduction is performed after image capture, then data is reduced to manageable size, but processing resources are wasted on unnecessary computation
Solution Approach 1:
The patent replaces the mechanical/computational reduction process with a physical optical filtering system. Instead of capturing complete images and then applying mathematical algorithms to reduce data, the system uses mask arrays with specific transmission patterns that physically filter light during capture. This substitution of optical mechanics for computational mathematics eliminates wasteful processing steps while achieving the same data reduction goal, improving overall system efficiency
Solution Approach 2:
The patent performs data reduction in advance during the capture process itself. The mask arrays are designed and positioned beforehand to block specific light paths, effectively performing the reduction operation before the sensor even detects the image. This preliminary reduction action eliminates the need for subsequent mathematical processing steps, saving computational resources and energy while maintaining productivity by capturing reduced data directly
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 significantly reduces the amount of data processed, saving power and processing capacity while maintaining image quality, making it suitable for devices with limited resources.
Implementation Method 1
Each sensor in the sensor array has an associated mask that blocks electromagnetic radiation from portions of the sensor
Implementation Method 2
the lenses are condensing lenses that focus the ambient light onto the sensor
Implementation Method 3
the sensors are photodiodes for detecting visible light
Data Source
AI summary
Some embodiments provide a novel compressive-sensing image capture device and a method of using data captured by the compressive-sensing image capture device. The novel compressive-sensing image capture device includes an array of sensors for detecting electromagnetic radiation. Each sensor in the sensor array has an associated mask that blocks electromagnetic radiation from portions of the sensor. In some embodiments, an array of passive masks is used to block a particular set of areas of each sensor in the sensor array. In some embodiments, the image capture device also includes an array of lenses corresponding to the sensors of the sensor array such that each sensor receives light that passes through a different lens. Some embodiments of the invention provide a dynamic mask array. In some embodiments, a novel machine trained network is provided that processes image capture data captured by the compressive-sensing image capture device to predict solutions to problems.