ROI Super-Resolution for Real-Time Mobile Image Detail
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Solution Overview
Problem
Existing super resolution processes are resource-intensive and time-consuming, making them unsuitable for real-time application on devices with limited processing power, such as mobile devices, and often require users to wait for offline processing, limiting interactive use.
Innovation Solution
A method that allows users to select a region of interest within an image, enabling super resolution only on that specific area, optimizing processing resources and allowing for quicker presentation of high-resolution details by aligning and merging low-resolution images without interpolation, and utilizing free CPU cycles for progressive super resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If super resolution is applied to the entire scene, then image quality is improved, but processing time and device resource consumption increase significantly
Solution Approach 1:
The patent divides the image processing task into segments by identifying and processing only the user-selected region of interest (ROI) at super resolution, while leaving the rest of the image at original resolution. This segmentation approach applies super resolution selectively to specific portions rather than the entire image, thereby reducing processing time and resource consumption while maintaining high quality where needed.
Solution Approach 2:
The patent implements local quality enhancement by applying super resolution processing only to the selected ROI rather than uniformly processing the entire image. This allows different parts of the image to have different quality levels - the ROI receives enhanced super resolution quality while other areas maintain standard quality, optimizing the balance between image quality and processing efficiency.
2Measurement precision
If super resolution is applied to the entire scene, then image quality is improved, but device resource consumption increases
Solution Approach 1:
The patent segments the processing workload by applying super resolution only to the ROI identified by the user. This reduces the total computational load and energy consumption compared to processing the entire image, while still delivering high quality results for the important region.
Solution Approach 2:
By enhancing quality locally only in the ROI rather than globally across the entire image, the patent reduces the overall device resource consumption and energy usage while maintaining high quality where the user needs it most.
3Measurement precision
If super resolution processing is performed offline, then image quality is improved, but user interaction and immediate feedback are limited
Solution Approach 1:
The patent performs super resolution processing on the selected ROI quickly and efficiently, making the results available immediately for user interaction rather than requiring lengthy offline processing. This preliminary fast processing enables users to interact with and provide feedback on the enhanced image in real-time.
Solution Approach 2:
The patent enables dynamic user interaction by allowing users to select different ROIs and have super resolution applied immediately to each selection. This dynamic approach contrasts with static offline processing, enabling flexible user control and immediate feedback loops.
Data Source
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AI summary
Systems, devices, and methods are described for efficiently super resolving a portion of an image. One embodiment involves capturing, using a camera module of a device, at least one image of a scene, and creating a higher resolution image of a user selected region of interest. The super resolution of the region of interest may be performed by matching a high resolution grid with a grid that is at the resolution of a device camera, populating the high resolution grid with information from an image from the camera, and then populating the remaining points of the grid that are not yet populated.