Weather Element Removal via Pure Dictionary Learning
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Solution Overview
Problem
Existing image processing technologies face challenges in accurately removing weather elements like rain, snow, and haze from images, especially in dynamic scenes captured by self-driving cars and surveillance systems, leading to degraded environmental perception and object recognition.
Innovation Solution
A method and apparatus that generate a pure weather element dictionary using machine learning algorithms on artificially generated or extracted pure weather element images, allowing for the accurate modeling and removal of weather elements from images containing both weather elements and background scenery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to remove weather elements, then the processing speed may be maintained, but the accuracy of weather element detection and removal deteriorates due to dynamic backgrounds and foreground objects
Solution Approach 1:
The patent segments the image processing task by separating weather element detection from background analysis. It divides the image into multiple regions and processes each region independently using different algorithms, thereby improving detection accuracy without proportionally increasing overall system complexity.
Solution Approach 2:
The patent implements dynamic adaptation by continuously updating the weather element model based on current image conditions. The system adjusts its parameters in real-time to account for changing weather patterns and scene dynamics, improving detection accuracy while maintaining computational efficiency through adaptive rather than static processing.
2Manufacturing precision
If advanced machine learning algorithms are applied to improve weather element removal accuracy, then the removal quality improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial processing by focusing computational resources only on regions containing weather elements rather than processing the entire image uniformly. It identifies and processes only the necessary portions of the image, thereby improving output quality while reducing overall processing time and computational burden.
Solution Approach 2:
The patent performs preliminary classification to identify image regions containing weather elements before applying complex removal algorithms. By pre-processing the image to locate and mark weather-affected areas, the system prepares the data structure for efficient targeted processing, reducing the computational scope and time required for the main removal operation.
3Productivity
If the system processes images in real-time to maintain operational continuity, then the operational efficiency is maintained, but the accuracy of weather element detection deteriorates due to dynamic scene changes
Solution Approach 1:
The patent implements periodic updates of the weather element model at strategically chosen intervals rather than continuous processing. This allows the system to maintain real-time operational throughput while periodically refreshing its detection accuracy with updated environmental data, balancing speed and precision requirements.
Solution Approach 2:
The patent maintains continuous image processing operations to ensure real-time operational throughput, while layering periodic accuracy refinement steps on top of this continuous baseline processing. This ensures that useful action (image processing) continues without interruption while still achieving high detection accuracy through periodic enhancement cycles.
Data Source
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AI summary
A method, apparatus and computer program product for removing weather elements, such as rain, from images are provided. In this regard, imagery from self-driving cars or video surveillance may be blurred by weather elements. The weather elements may be removed by utilizing pure weather element images. The pure weather element images may be processed by machine learning techniques to model pure weather element data, and generate a pure weather element dictionary. Imagery including both weather elements and background scenery may then be processed accordingly, and the weather elements removed based on the learned pure weather element dictionary. Resulting weather-free images may then be generated.