Neural Network Bright Spot Removal in Image Capture
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Solution Overview
Problem
Existing image capture technologies fail to effectively remove bright spots, glare, and noise from images, leading to reduced image quality, especially in high light or low light environments, which affects various applications including photography and autonomous vehicle imaging.
Innovation Solution
A method using a fully convolutional neural network for image denoising, which identifies and mitigates bright spots through sensor-specific profiling and post-processing, allowing for the recovery of underlying image details and reduction of noise, thereby enhancing image quality and enabling better performance in noisy environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If standard digital imaging or photographic techniques are used, then the imaging system is simple and easy to operate, but the dynamic range of luminosity is limited and bright spots cause loss of image details
Solution Approach 1:
The imaging system is segmented into multiple sensors with different exposure times, allowing each sensor to capture different luminosity ranges. This segmentation enables the system to handle a broader dynamic range without requiring a completely complex new imaging architecture.
Solution Approach 2:
A neural network serves as an intermediary that processes the raw images from multiple sensors, automatically selecting and merging the most appropriate image data for each region. This intermediary handles the complexity of dynamic range merging, keeping the overall system relatively simple while achieving extended luminosity range.
2Illumination intensity
If multiple low dynamic range images with different exposure times are merged using HDR imaging, then the dynamic range of luminosity is improved, but the processing complexity and time are increased
Solution Approach 1:
Multiple images with different exposure times are captured in advance before the final processing step. This preliminary capture of multiple exposures allows the neural network to work with pre-prepared data, reducing the actual processing time when the final image needs to be generated.
Solution Approach 2:
The neural network performs automatic selection and merging of image data without requiring manual intervention or complex processing steps. The system serves itself by intelligently determining which image regions to use from which exposure, significantly reducing processing time compared to traditional HDR methods.
3Measurement precision
If traditional post-processing techniques are used to remove glare, then the process is simple to implement, but the effectiveness in recovering image details is insufficient
Solution Approach 1:
Traditional mechanical or algorithmic glare removal methods are replaced with a neural network-based system. The neural network learns to identify and remove glare artifacts while preserving image details, achieving superior precision without requiring complex manual processing steps.
Solution Approach 2:
The processing system changes parameters dynamically by adjusting the neural network's processing based on the specific characteristics of each image region. This allows the system to adapt to different glare conditions and achieve high detail recovery accuracy without using a fixed, complex processing pipeline.
4Object-affected harmful factors
If sensor masks or hardware are used to reduce glare, then the glare reduction is effective, but the device complexity and cost are increased
Solution Approach 1:
Physical sensor masks and hardware glare reduction mechanisms are replaced with a software-based neural network approach. The neural network processes images to remove glare effects without requiring any additional physical components, thereby reducing device complexity while maintaining effectiveness.
Solution Approach 2:
Instead of modifying the physical sensor with masks, the system creates a virtual correction by processing a copy of the captured image through the neural network. This allows glare reduction to be achieved through data processing rather than physical modification, keeping the hardware simple.
Data Source
AI summary
A method for image capture includes identifying a bright spot in an image. A neural network is used to recover details in bright spot area through a trained de-noising process. Post-processing of the image is conducted to match image parameters of recovered details in the bright spot area to another area of the image.