Image Processing Subsampling Neural Network Reconstruction
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Solution Overview
Problem
Existing image processing apparatuses and techniques face challenges such as high costs, increased power consumption, inability to capture the entire field of view at full resolution and frame rate, resulting in image artifacts, and failure to maintain visual clarity, which hampers immersive extended-reality (XR) experiences.
Innovation Solution
The proposed apparatus and method utilize sub-sampling to capture images with reduced pixel counts and employ pre-trained neural networks to reconstruct and enhance memory features, thereby generating output images with improved visual quality and reduced processing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex image processing is used to maintain visual clarity, then image quality is improved, but power consumption and cost increase
Solution Approach 1:
The image processing is segmented into two distinct stages: a first image processing stage that performs basic processing with low power consumption, and a second image processing stage that performs enhanced processing only when needed. This segmentation allows the system to maintain acceptable image quality while significantly reducing overall power consumption by avoiding continuous high-power processing.
Solution Approach 2:
The system dynamically changes processing parameters based on image content analysis. When an image is determined to contain memory features, the processing parameters are adjusted to apply more sophisticated reconstruction algorithms. When no memory features are present, the system uses simpler processing modes, thereby adapting power consumption and processing intensity to the actual needs of each image.
2Manufacturing precision
If full resolution and frame rate capture is implemented, then image quality is improved, but device complexity and cost increase
Solution Approach 1:
The system applies partial action by performing full-resolution processing only on specific regions of interest (memory features) rather than the entire image. For other regions, reduced processing is applied. This selective approach maintains image quality where it matters most while reducing overall device complexity and computational requirements.
Solution Approach 2:
The system uses a two-stage processing approach where the first stage produces a preliminary processed image, and the second stage selectively refines specific regions. This copying and refinement approach allows the system to achieve high-quality results for memory features without requiring the entire system to operate at full complexity continuously.
3Manufacturing precision
If advanced image processing is used to prevent artifacts, then image quality is improved, but processing time increases
Solution Approach 1:
The first image processing stage performs preliminary processing on all images before they are analyzed for memory features. This preliminary action prepares the images in advance so that when memory features are detected, the second stage can focus computational resources efficiently on reconstruction tasks, reducing overall processing time while maintaining quality.
Solution Approach 2:
The system dynamically adjusts processing intensity based on real-time analysis of image content. When memory features are detected, the system transitions to a more intensive second processing stage. When no memory features are present, the system remains in the lighter first processing stage. This dynamic adaptation prevents unnecessary processing time expenditure while ensuring quality when needed.
Data Source
AI summary
Disclosed is an apparatus, including an image sensor to capture an image with a first number of pixels; execute a sub-sampling during the capture of the image to store a sub-sampled input image comprising a second number of pixels less than the first number of pixels. A processor configured to execute a pre-trained neural network model on the sub-sampled input image to detect one or more memory features in the sub-sampled input image and reconstruct missing or sub-sampled pixels corresponding to the detected one or more memory features in the sub-sampled input image; and generate an output image with enhanced one or more memory features present in a legible form.