Multi-Lens Camera System for Distortion-Free Imaging
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Solution Overview
Problem
Conventional image generation algorithms fail when input images are distorted by noise or obscurities, such as raindrops or blur, leading to reduced accuracy or failure in decision-making systems, particularly in vehicle-based imaging systems like ADAS and self-driving cars.
Innovation Solution
A camera system with multiple lenses, each equipped with a dedicated sensor, uses deep learning techniques to generate a single distortion-free image by mapping sets of low-resolution images, including distorted ones, to an associated high-resolution image, effectively combining and weighting pixels to discard or ignore distorted areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image generation algorithms are used, then the system is simple to operate, but the algorithm fails when input images are distorted by noise or obscurities
Solution Approach 1:
The system divides the imaging function into multiple independent lens-sensor units, where each unit captures images independently. This segmentation allows the system to process multiple distorted images separately and combine them to produce a single distortion-free image, thereby improving reliability without requiring complex algorithms to handle each image individually.
Solution Approach 2:
The system merges multiple distorted images captured by different lenses into a single distortion-free image. By combining the information from multiple sources, the system overcomes the limitations of individual distorted images and achieves reliable image generation even when some lenses are obscured.
2Reliability
If a single lens is used, then the device complexity is low, but the image quality deteriorates when exposed to external environment factors like rain drops
Solution Approach 1:
Instead of using a single lens that is vulnerable to environmental factors, the system segments the imaging function across multiple lenses. Each lens captures images independently, so if one lens is obscured by rain drops or other factors, the other lenses continue to capture clear images, ensuring overall system reliability.
Solution Approach 2:
The system applies local quality by having each lens-sensor unit operate independently with its own dedicated sensor. This allows each unit to optimize for its specific viewing angle and environmental conditions, and the final image is constructed by combining the best quality regions from multiple sources.
3Measurement precision
If multiple lenses with dedicated sensors are used, then the system can generate distortion-free images, but the device complexity increases
Solution Approach 1:
The system uses multiple lens-sensor units that independently capture images, segmenting the imaging task across multiple simple components rather than relying on a single complex system. This segmentation enables high measurement precision through data fusion while keeping individual components relatively simple.
Solution Approach 2:
The system creates multiple copies of the lens-sensor unit, each capturing the same scene from slightly different perspectives. These copies are then processed and combined to generate a single high-accuracy image, leveraging redundancy to improve measurement precision without requiring each individual unit to be highly complex.
4Adaptability or versatility
If distorted images are used as input, then the system can operate in harsh environments, but the decision-making accuracy deteriorates
Solution Approach 1:
The system converts the harmful effect of environmental obstructions into a benefit by using multiple lenses. When some lenses are distorted by rain drops or mud, the system identifies and weights the clear images from other lenses more heavily, transforming the presence of distorted images into an opportunity to select and combine only the high-quality portions for accurate decision-making.
Solution Approach 2:
The system captures images with all lenses regardless of their quality, performing an excessive action by collecting more data than strictly necessary. Then, it selectively uses only the portions of images that meet quality thresholds, discarding or de-weighting distorted regions. This partial usage of captured data ensures decision-making accuracy while maintaining environmental adaptability.
Data Source
AI summary
A camera for generating distortion free images and a method thereof is disclosed. The camera includes a plurality of lenses, wherein each of the plurality of lenses has a dedicated sensor. The camera further includes a processor communicatively coupled to the plurality of lenses. The camera further includes a memory communicatively coupled to the processor and having instructions stored thereon, causing the processor, on execution to capture a plurality of images through the plurality of lenses and to generate a single distortion free image from the plurality of images based on a deep learning technique trained using a mapping of each of a plurality of sets of low resolution images generated in one or more environments to an associated distortion free image, wherein one or more low resolution images in each of the plurality of sets are distorted.