Transient Element Removal in Image Processing
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Solution Overview
Problem
Manually removing transient elements, such as moving objects, from images or videos is difficult and time-consuming, as it requires aligning and analyzing pixel values across multiple frames to identify and exclude transient objects while preserving static scene elements.
Innovation Solution
A system that aligns visual content from multiple images captured at different moments, identifies transient pixels based on pixel value changes, and generates output images using non-transient pixel values to exclude transient objects, utilizing a processor and electronic storage for image processing and alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual methods are used to remove transient elements from images, then the user can control the removal process, but the process becomes difficult and time-consuming
Solution Approach 1:
The system automatically identifies and removes transient elements without requiring manual user intervention. The processor autonomously analyzes pixel value changes across multiple images, identifies transient pixels, and generates output images with transient elements removed, allowing the system to serve itself rather than requiring manual operation.
Solution Approach 2:
The patent replaces manual mechanical operations with an automated computational system. Instead of manually analyzing and editing images, the system uses processors to automatically detect transient elements through pixel value comparison and algorithmically remove them, substituting human manual work with automated image processing mechanisms.
2Productivity
If automated processing is used to remove transient elements, then the process becomes faster and more efficient, but the complexity of the system increases
Solution Approach 1:
The image processing task is segmented into distinct operational stages: aligning visual content from multiple images, identifying transient pixels through pixel value change detection, determining non-transient pixel values, and generating output images. This segmentation allows each stage to be processed independently and efficiently by the processor, managing complexity through structured decomposition of the overall task.
Solution Approach 2:
The system transitions from analyzing single images to analyzing multiple images in temporal sequence. By capturing and comparing images at different moments, the system adds a temporal dimension to the analysis, enabling automatic identification of transient elements through pixel value changes across the image sequence, thereby improving productivity through multi-frame processing.
3Measurement precision
If multiple images are aligned and analyzed to identify transient pixels, then the accuracy of transient element removal improves, but the processing complexity and time increase
Solution Approach 1:
The system performs preliminary alignment of visual content from multiple images before conducting transient pixel identification. By pre-aligning the images so that corresponding pixels represent the same scene locations, the system establishes a foundation for accurate transient detection, reducing the complexity of subsequent analysis while improving measurement precision.
Solution Approach 2:
The system uses pixel value changes across aligned images as feedback to identify transient pixels. By comparing pixel values at corresponding locations across multiple images and detecting significant changes, the system automatically determines which pixels represent transient elements, improving accuracy through iterative comparison and feedback from the image sequence analysis.
Data Source
AI summary
Captured images of a scene may include depictions of objects moving within the scene. The portions of the images depicting the moving objects may be identified by aligning the images and analyzing the changes in pixel values of the aligned images. For the portion of the images depicting the moving objects, the pixels values may be replaced with mean, mode, and/or median values that approximate the value that would have been captured without the moving objects, and one or more image without the depiction of moving objects may be generated.