Compressed-Domain Camera Control Without Image Reconstruction
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Solution Overview
Problem
Compressive sensing capturing devices face challenges in implementing automatic camera features like autofocus due to the computationally intensive reconstruction required to retrieve the original image from compressed measurements, which is slow and battery inefficient.
Innovation Solution
Employing a machine learning algorithm based on an artificial neural network, specifically a convolutional neural network, to directly measure image features from compressed sensing data, allowing for faster and more efficient automatic adjustments of device attributes such as autofocus without reconstructing the original image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If compressive sensing is used to reduce measurements and storage, then battery usage and storage requirements are reduced, but computationally intensive reconstruction is required which is slow and battery inefficient
Solution Approach 1:
The patent extracts and processes only the essential features needed for automatic camera controls from the compressed measurements, rather than performing full image reconstruction. This selective extraction of relevant information (edge detection, gradient calculation, focus metric computation) from the compressed domain eliminates the computationally intensive reconstruction step while maintaining the ability to perform autofocus and other automatic functions.
Solution Approach 2:
The patent inverts the traditional workflow by performing image processing operations directly in the compressed measurement domain rather than reconstructing the image first. Instead of following the conventional path of compressed sensing → full reconstruction → image processing, the patent implements image processing → compressed sensing operations, allowing automatic camera controls to be executed on the compressed data itself.
2Quantity of substance
If compressive sensing is used to capture images with fewer measurements, then storage requirements are reduced, but the original image is not available for direct processing
Solution Approach 1:
The patent introduces an intermediary processing stage that operates on compressed measurements to extract image attributes and control parameters. This intermediary layer includes operations such as computing gradients, detecting edges, and calculating focus metrics directly from the compressed data, serving as a bridge between the compressed sensing domain and the automatic camera control functions without requiring full image reconstruction.
3Measurement precision
If traditional compressive sensing reconstruction is performed to retrieve the original image, then complete image data is obtained, but the process is computationally intensive and slow
Solution Approach 1:
The patent applies partial action by performing only the necessary image processing operations needed for automatic camera controls on the compressed measurements, rather than completing the full reconstruction process. This partial processing approach extracts sufficient information for autofocus, exposure, and other automatic functions without the excessive computational burden of obtaining the complete reconstructed image.
Data Source
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AI summary
A compressive sensing capturing device has circuitry, which is configured to obtain compressive sensing image data; and to set a device attribute based on image attribute data, wherein the image attribute data are based on a machine learning algorithm performed in the compressing domain on the obtained compressive sensing image data.