Deep Convolutional Image Up-Sampling for Vehicle Localization
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Solution Overview
Problem
High-resolution images captured by consumer vehicles are often down-sampled due to bandwidth constraints, leading to unreliable feature detection and incorrect localization determinations when up-sampled, as common up-sampling techniques result in artifacts like blurring of features.
Innovation Solution
An up-sampling network, potentially a deep neural network, is trained to generate a higher resolution image from down-sampled images or temporal sequences, preserving sharp features by determining appropriate network weights based on the scale of the images and performing interpolation or convolution to avoid global blurring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If down-sampling is applied to high-resolution images for bandwidth-constrained transmission, then bandwidth consumption is reduced, but feature detection reliability deteriorates
Solution Approach 1:
The system performs down-sampling in advance before transmission to reduce bandwidth consumption, while preserving the capability to reconstruct high-resolution images later. The down-sampled images are transmitted to cloud-based computing networks where they can be up-sampled using deep convolutional networks to recover feature details for reliable detection.
Solution Approach 2:
A deep convolutional up-sampling network acts as an intermediary between down-sampled low-resolution images and the required high-resolution images. This neural network intermediary learns to reconstruct fine details and sharp features that are lost during down-sampling, enabling reliable feature detection without transmitting full-resolution images.
2Area of stationary object
If common up-sampling techniques are applied to down-sampled images, then image resolution is increased, but image quality deteriorates due to artifacts and blurring
Solution Approach 1:
Traditional mechanical up-sampling methods (like bilinear or bicubic interpolation) are replaced with a deep convolutional neural network. This neural network-based approach substitutes conventional signal processing with learned patterns from training data, enabling reconstruction of sharp features and realistic details that mechanical methods cannot achieve.
Solution Approach 2:
The system changes the parameters of the up-sampling process by using learnable convolution kernels instead of fixed interpolation formulas. The neural network adjusts its weights and filters based on training data to optimize the reconstruction quality, dynamically adapting to different image content and down-sampling factors rather than applying uniform mechanical rules.
3Measurement precision
If high-resolution images are transmitted in full resolution, then feature detection accuracy is improved, but bandwidth consumption increases
Solution Approach 1:
Instead of transmitting full-resolution images, the system performs preliminary down-sampling before transmission to conserve bandwidth. The high-resolution reconstruction is performed later at the receiving end using deep convolutional networks, achieving both bandwidth efficiency and feature detection accuracy through deferred processing.
Solution Approach 2:
A deep convolutional up-sampling network serves as an intermediary that reconstructs high-resolution images from down-sampled versions. This intermediary process enables the system to work with compact low-resolution transmitted data while recovering the detailed information needed for accurate feature detection, eliminating the need to transmit full-resolution images.
Data Source
AI summary
An image and/or temporal sequence of images is received. The image and/or sequence of images was captured by an image capturing device of a vehicle apparatus onboard a vehicle and was down-sampled thereby. A scale of the image(s) is determined. An up-sampling network receives the image(s) and the scale. The up-sampling network determines appropriate network weights based on the scale. Based on the appropriate network weights, the up-sampling network generates a higher resolution image having a pre-defined scale.