Image Mastering Spatial-Temporal Noise Reduction
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Solution Overview
Problem
Imaging systems face limitations due to noise artifacts and resolution constraints from capture device components, leading to suboptimal signal-to-noise ratios and line pair frequencies, which affect image quality and efficiency.
Innovation Solution
An image processing system applies computational spatial-temporal analysis to assess pixels between temporal and perspective views, enhancing signal-to-noise ratios and line pair frequencies by modifying image data through vector analysis and pixel replacement techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional capture systems are used, then device complexity is reduced, but signal-to-noise ratio and resolution deteriorate
Solution Approach 1:
The system performs preliminary spatial-temporal analysis on multiple captured frames before final image generation. By pre-processing the image data to identify and correct noise patterns across temporal sequences, the system improves signal-to-noise ratio before the final output is produced, resolving the contradiction between maintaining simple capture hardware and achieving high image quality.
Solution Approach 2:
The patent introduces computational processing as an intermediary between the simple capture system and the final high-quality image output. This intermediary layer performs spatial-temporal analysis and pixel manipulation to enhance resolution and reduce noise, allowing the system to achieve high measurement precision without requiring complex capture hardware.
2Manufacturing precision
If traditional capture systems are used, then device complexity is reduced, but line pair frequency and resolution deteriorate
Solution Approach 1:
The system transitions from analyzing images in a single spatial dimension to utilizing temporal dimension by processing sequences of frames. By incorporating time as an additional dimension for analysis, the system can extract higher frequency information and improve line pair frequency beyond what traditional single-frame processing could achieve, while keeping the capture hardware simple.
Solution Approach 2:
The system performs preliminary spatial-temporal analysis on multiple captured frames before final image generation. By pre-processing the image data to identify and correct noise patterns across temporal sequences, the system improves signal-to-noise ratio before the final output is produced, resolving the contradiction between maintaining simple capture hardware and achieving high image quality.
3Measurement precision
If computational spatial-temporal analysis is applied, then signal-to-noise ratio improves, but processing time increases
Solution Approach 1:
The computational processing is divided into distinct segments: spatial analysis, temporal analysis, and pixel replacement operations. By segmenting the processing pipeline, the system can optimize each stage independently and perform operations in parallel where possible, reducing overall processing time while maintaining the signal-to-noise ratio improvements.
Solution Approach 2:
The system applies pixel replacement only to specific regions identified as needing enhancement rather than processing the entire image uniformly. This partial action approach reduces the total computational burden and processing time while still achieving the desired signal-to-noise ratio improvement in critical areas.
Data Source
AI summary
Systems, devices, and methods disclosed herein may apply a computational spatial-temporal analysis to assess pixels between temporal and/or perspective view imagery to determine imaging details that may be used to generate image data with increased signal-to-noise ratio.