Camera Image Brightening Using Confidence-Based Reconstruction
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Solution Overview
Problem
Existing image brightening methods, both classic and machine learning-based, often fail to adequately address variable lighting conditions, leading to poor-quality images with artifacts and insufficient consideration of illumination disparities, which can impact safety-critical applications like driver assistance systems.
Innovation Solution
A method that determines confidence levels for different image brightening techniques, allowing reconstruction of an output image based on these confidences to optimize brightening quality, minimizing artifacts and adapting to variable lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If classic filter-based image preprocessing methods are used to brighten images, then the entire bit and dynamic range is exploited for brightness adjustment, but the methods can be difficult to implement in embedded systems and may not adequately handle variable lighting conditions
Solution Approach 1:
The image is divided into multiple regions with different lighting conditions, and each region is processed separately using appropriate brightening methods. This allows the system to handle variable lighting conditions effectively while maintaining manageable computational complexity for embedded systems.
Solution Approach 2:
The brightening method dynamically adapts to different lighting conditions by detecting local illumination disparities and adjusting processing parameters accordingly. This dynamic adaptation enables the system to handle diverse lighting scenarios without requiring overly complex fixed algorithms.
2Reliability
If machine learning-based brightening methods are used, then image context and textures are modeled to improve brightening quality, but the computational requirements increase and may not be suitable for real-time processing in embedded systems
Solution Approach 1:
The system performs preliminary detection of lighting conditions and image characteristics before applying brightening operations. This preliminary analysis allows the selection of appropriate processing methods and parameters, ensuring high quality results while optimizing computational efficiency for real-time processing.
Solution Approach 2:
Different processing strategies are applied to different regions of the image based on local characteristics. Regions with high reliability requirements receive more sophisticated processing, while other regions use simpler methods, thereby balancing quality and processing speed.
3Ease of operation
If existing brightening methods are applied without considering illumination disparities, then processing is simplified, but artifacts are introduced and image quality deteriorates in regions with varying lighting conditions
Solution Approach 1:
The system applies different processing parameters to different regions based on local illumination characteristics. This localized processing approach maintains simplicity in individual region handling while preventing artifacts that would result from uniform processing across the entire image.
Solution Approach 2:
The brightening parameters are dynamically adjusted based on detected illumination disparities in different image regions. By changing parameters locally rather than applying fixed parameters globally, the system maintains processing simplicity while eliminating artifacts caused by ignoring lighting variations.
Data Source
AI summary
A computer-implemented method is disclosed for brightening an image of a camera, the use of the method for use in an application in the field of computer vision or a system for data processing. The method includes: providing at least one raw data image of the camera; providing at least one first brightening image of the raw data image, wherein the first brightening image is brightened by means of a first brightening method; determining a first confidence for the brightening of the raw data image of the camera by the first brightening method; and reconstructing an output image at least in part from the raw data image and/or the first brightening image as a function of the first confidence.