Adaptive Image Filtering via Region Proposal Networks
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Solution Overview
Problem
Existing image processing systems consume significant energy for filtering and processing images, which is not optimized for computer vision tasks, leading to inefficiencies and data loss.
Innovation Solution
The proposed solution involves using a Region Proposal Network (RPN) to process raw image data and selectively apply adaptive filtering to specific regions of interest (ROIs) based on their probability of containing objects, thereby reducing unnecessary processing and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full image filtering is applied to the entire image, then image quality for human viewing is improved, but energy consumption increases and data loss occurs for computer vision tasks
Solution Approach 1:
The image is divided into multiple regions of interest (ROIs) based on object detection probabilities. Full filtering is applied only to regions with high probability of containing objects, while regions with low probability receive minimal or no filtering. This segmentation approach reduces overall energy consumption while maintaining object detection accuracy in critical areas.
Solution Approach 2:
Different filtering strategies are applied to different regions of the image based on their importance for object detection. High-probability regions receive comprehensive filtering to ensure detection accuracy, while low-probability regions receive reduced filtering. This local quality approach optimizes the balance between energy consumption and detection reliability.
2Reliability
If full image filtering is applied to the entire image, then image quality is improved, but processing time increases
Solution Approach 1:
The image processing is segmented into different regions with different processing priorities. Regions identified as high-probability ROIs undergo full filtering to ensure accurate object detection, while other regions receive minimal processing. This segmentation reduces the total processing time while maintaining detection accuracy in critical regions.
Solution Approach 2:
Instead of applying full filtering uniformly across the entire image, the system applies partial filtering only to necessary regions. This partial action approach processes only the portions of the image that are likely to contain objects, significantly reducing processing time while maintaining sufficient accuracy for object detection tasks.
3Reliability
If extensive filtering is applied to all regions, then image quality for human viewing is improved, but unnecessary processing is performed for computer vision
Solution Approach 1:
The system performs preliminary object detection on the raw image to identify high-probability regions of interest before applying extensive filtering. This preliminary action allows the system to focus subsequent filtering operations only on regions likely to contain objects, improving processing efficiency while maintaining detection accuracy. The preliminary detection step guides the selective application of filtering resources.
Solution Approach 2:
The system applies filtering selectively rather than uniformly across the entire image. By performing partial filtering only on identified regions of interest, the system avoids excessive processing in areas unlikely to contain objects. This approach maintains high processing efficiency while ensuring sufficient image quality for object detection in critical regions.
Data Source
AI summary
An image filtering arrangement comprising a controller configured to receive an image data file; propose zero or more regions of interest for the image data file; and to select adaptive filtering for at least one of the proposed zero or more regions of interest and apply the selected adaptive filtering to the at least one of the proposed zero or more regions of interest.