Vehicle Image Enhancement via Adaptive Filter Selection
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Solution Overview
Problem
Vehicle camera systems face quality issues due to sudden changes in lighting conditions, such as traveling through tunnels or weather changes, leading to incorrect decisions in assistance systems.
Innovation Solution
A method and device for automatic image enhancement using a camera, image processing filters, and a learning neural network to transform primary images into intermediate images, determine quality indices, and select the highest quality image, with a learning phase to adaptively select the best image processing filter for improved image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple image processing filters are applied to handle varying lighting conditions, then image quality under different lighting conditions is improved, but device complexity increases
Solution Approach 1:
The system dynamically selects from multiple image processing filters based on the specific lighting conditions detected in the primary image. Instead of applying all filters or using a fixed filter, the system adapts its processing approach by choosing the most appropriate filter for each situation, thereby maintaining image quality while managing complexity through conditional selection rather than comprehensive application
Solution Approach 2:
The system changes processing parameters by selecting different filters based on lighting conditions. The filter selection module evaluates the primary image characteristics and adjusts the processing parameters by applying the most suitable filter from the plurality of available filters, allowing the system to optimize image quality for specific lighting scenarios without maintaining all processing capabilities simultaneously active
2Adaptability or versatility
If a learning neural network is used to adaptively select image processing filters, then adaptability to different lighting conditions is improved, but device complexity and computational requirements increase
Solution Approach 1:
The learning neural network is trained in advance during a learning phase to recognize patterns and determine which image processing filters are most effective for specific lighting conditions. This preliminary training allows the system to develop a knowledge base that enables rapid, adaptive filter selection during actual operation without requiring complex real-time analysis, thereby improving adaptability while managing computational complexity through pre-computed knowledge
3Reliability
If image processing filters are applied to enhance image quality, then assistance system decision accuracy is improved, but processing time increases
Solution Approach 1:
The system dynamically selects only the necessary image processing filter based on the specific lighting conditions, avoiding the application of all available filters. This conditional selection approach ensures that image quality is enhanced sufficiently for accurate assistance system decisions while minimizing processing time by applying only the most appropriate filter rather than multiple filters sequentially
Data Source
AI summary
A device and method for automatic image enhancement in vehicles, in particular land vehicles, including a camera to record a primary-image, and an image-processing-module to determine a resulting-image from the primary-image. The image-processing-module includes image-processing-filters, each configured to transform the primary-image in each case into an intermediate-image, an evaluation-module that outputs a quality-index for each of the intermediate-images transformed with the image-processing-filter, a selection-module that selects the intermediate-image having the highest quality index and outputs it as the resulting-image, and a learning-neural-network to learn, in a learning phase, for each primary-image the image-processing-filter, from the image-processing-filters, having the highest quality index of the intermediate-image, and after the learning phase, for each primary-image to select the image-processing-filter, from the image-processing-filters, having the highest quality index of the intermediate-image.