Camera Accessory Masking for Mobile Image Processing
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Solution Overview
Problem
Current mobile devices lack the processing power and efficiency to timely remove camera accessories from 360-degree camera images without consuming excessive battery life, often resulting in blurred images due to movement during capture.
Innovation Solution
A method that captures images using a camera device, identifies camera accessories via received identifying information or QR codes, and removes the accessory's presence from the image using a processor, employing a virtual mask to blend surrounding areas and reduce apparent effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image processing is performed on mobile devices to remove camera accessories, then image quality is improved, but processing time increases and battery consumption increases
Solution Approach 1:
The system performs preliminary identification of camera accessories in the captured image before executing the removal process. By detecting the accessory presence first and preparing removal parameters in advance, the system optimizes the subsequent removal operation to execute faster while maintaining high image quality.
Solution Approach 2:
The image processing is divided into distinct segments: accessory identification phase, mask generation phase, and removal/blending phase. This segmentation allows each stage to be optimized independently, reducing overall processing time while preserving image quality through specialized algorithms for each task.
2Manufacturing precision
If image processing is performed on mobile devices to remove camera accessories, then image quality is improved, but battery consumption increases
Solution Approach 1:
The system applies partial action by selectively processing only the portions of the image containing camera accessories rather than the entire image. The masking technique confines computational resources to relevant regions, significantly reducing battery consumption while maintaining image quality in processed areas.
Solution Approach 2:
The system extracts and isolates the camera accessory from the main image content using identification and masking techniques. By separating the accessory removal task from the rest of the image processing, the system minimizes computational overhead and battery usage while preserving the quality of the remaining image.
3Productivity
If image capture is performed during movement, then productivity is improved, but image quality deteriorates due to blur
Solution Approach 1:
The system converts the harmful effect of movement-induced blur into a benefit by using the detected accessory patterns and identifying information to guide the removal process. Even blurry images containing accessory identifiers can be processed to remove the accessory, and the blending algorithms compensate for motion artifacts to restore image quality.
Solution Approach 2:
The system introduces an intermediary processing stage that uses identifying information about the camera accessory as a mediator between the captured image and the final output. This intermediary step allows the system to selectively remove accessories even when the overall image quality is degraded by motion, preserving the useful content while eliminating the harmful accessory elements.
Data Source
AI summary
One embodiment provides a method, including: capturing, using a camera device, an image, wherein the image comprises at least one portion of a camera accessory attached to the camera device; identifying, using a processor, the camera accessory; and removing, based on the identification of the camera accessory, the at least one portion of the camera accessory from the image. Other aspects are described and claimed.