Real-time Image Reconstruction via FPGA Convolution Encoding
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Solution Overview
Problem
Current enhanced vision systems for aircraft face limitations in providing accurate images during low-light or obstructed conditions, such as twilight or fog, due to sparse and varying input data from sensors like video cameras and lidar, leading to degraded image quality and potential distractions for pilots.
Innovation Solution
A computationally-efficient image reconstruction method using a parallel-based processor like a field programmable gate array (FPGA) that performs convolution-based point-sample encoding and interpolation to reconstruct image structure and detail from sparse data, allowing for real-time reconstruction with low latency even for large images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sparse sensor data is used for image reconstruction in low-light conditions, then the system can operate in challenging environmental conditions, but the image quality is degraded and details are lost
Solution Approach 1:
The patent introduces an intermediary computational process (convolutional neural network-based reconstruction algorithm) that mediates between the sparse sensor data and the final image output. This intermediary processing layer transforms the limited input data into a high-quality reconstructed image by filling in missing details through learned patterns from training data, thus resolving the contradiction between operating in challenging conditions and maintaining image quality
Solution Approach 2:
The system performs preliminary action by pre-training reconstruction algorithms on large datasets of paired sparse and complete images before actual operation. This preliminary training phase enables the system to learn effective reconstruction strategies in advance, allowing it to compensate for sparse input data during real-time operation in challenging environmental conditions while maintaining high image quality
2Loss of information
If traditional image reconstruction methods are used, then the system can provide visual information to pilots, but the reconstruction accuracy is insufficient and anomalies may distract the pilot
Solution Approach 1:
The patent changes the parameter of reconstruction methodology from traditional algorithms to deep learning-based convolutional neural networks. This parameter change enables the system to achieve superior reconstruction accuracy by leveraging learned features and patterns, thereby providing accurate visual information to pilots without distracting anomalies while still operating in various environmental conditions
3Measurement precision
If computationally intensive reconstruction algorithms are used, then image quality can be improved, but processing time increases and latency is high
Solution Approach 1:
The system performs computationally intensive work in advance through offline training of deep learning models on large datasets. Once trained, the models contain compressed knowledge that can be rapidly applied during real-time operation. This preliminary action separates the heavy computational burden from real-time processing, enabling high image quality reconstruction with low latency during actual flight operations
Solution Approach 2:
The patent replaces traditional mechanical/computational image reconstruction methods with a learned model-based approach. The convolutional neural network substitutes conventional iterative algorithms, providing faster processing speeds while maintaining or improving reconstruction quality, thus resolving the contradiction between image quality and processing time
4Measurement precision
If more sensor data is collected to improve image quality, then reconstruction accuracy can be enhanced, but the system complexity and cost increase
Solution Approach 1:
The patent uses copying by training the reconstruction algorithm on copies of complete images alongside sparse sensor data during the offline training phase. The neural network learns to map sparse inputs to complete images by studying numerous training examples, enabling accurate reconstruction without requiring additional physical sensors or increasing system hardware complexity
Data Source
AI summary
An apparatus is provided that includes a processor (e.g., FPGA) configured to cause the apparatus to perform a number of operations. The apparatus may be caused to receive input data for a digital image represented by point samples at respective sample locations, reconstruct the digital image from the input data for presentation by a display including display pixels, and output the reconstructed digital image. The reconstruction may include a number of operations for each of at least some of the display pixels. In this regard, the apparatus may be caused to perform a convolution-based, point-sample encoding of a selected display pixel to generate an encoding that identifies point samples of the digital image nearby the selected display pixel. And the apparatus may be caused to interpolate a value of the selected display pixel from at least some of the identified point samples using the generated encoding.