Foveated Image Processing With Row-Wise Multi-Stage Denoising
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Solution Overview
Problem
Conventional multi-resolution denoising techniques are computationally expensive and introduce latency, making them unsuitable for real-time applications like video see-through systems and extended reality, where they fail to match image-capture and display rates, causing user discomfort and sickness.
Innovation Solution
A multi-stage image-processing technique that processes foveated image data using kernels in a row-by-row fashion, allowing for real-time multi-resolution denoising by passing row-by-row results from lower-resolution to higher-resolution contexts, reducing post-processing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional multi-resolution denoising techniques are used, then signal-to-noise ratio is improved, but processing time and latency increase
Solution Approach 1:
The patent segments the image processing into multiple resolution stages (low-resolution, medium-resolution, high-resolution) and processes them in sequence. Each stage processes a different resolution version of the image, allowing noise reduction to be performed efficiently at lower resolutions first, then refined at higher resolutions. This segmentation enables the system to achieve good denoising performance while reducing overall processing time compared to conventional single-stage approaches.
Solution Approach 2:
The patent performs preliminary denoising at lower resolutions before processing the high-resolution image. By pre-processing the downsampled versions of the image and using their denoised results as guidance for the final high-resolution denoising, the system performs useful work in advance. This preliminary action at lower resolutions reduces the computational burden on the final high-resolution processing stage, thereby reducing total processing time while maintaining signal-to-noise ratio improvement.
2Measurement precision
If conventional multi-resolution denoising techniques are used, then signal-to-noise ratio is improved, but processing speed decreases
Solution Approach 1:
The patent divides the denoising task into segmented resolution stages, processing images at low, medium, and high resolutions separately. This segmentation allows the system to perform computationally intensive denoising operations at lower resolutions where fewer pixels require processing, thereby increasing overall processing speed while still achieving effective noise reduction that carries over to the final high-resolution output.
Solution Approach 2:
The patent performs denoising actions partially at each resolution stage rather than applying full denoising at the final high resolution. By performing partial denoising at lower resolutions and using those results to guide the final high-resolution processing, the system achieves effective noise reduction with less total computational effort, thereby increasing processing speed while maintaining signal-to-noise ratio improvement.
3Measurement precision
If conventional multi-resolution denoising techniques are used, then noise reduction is achieved, but power consumption increases
Solution Approach 1:
The patent segments the noise reduction process into multiple resolution stages, performing denoising operations on downsampled versions of the image before final high-resolution processing. This segmentation reduces the total number of pixels that require intensive processing, thereby reducing power consumption while still achieving effective noise reduction at the final output through the cascaded resolution stages.
Solution Approach 2:
The patent performs preliminary noise reduction at lower resolutions before final high-resolution processing. By pre-processing the downsampled images and using their denoised results as guidance, the system performs useful noise reduction work in advance when computational requirements are lower, reducing the power needed for the final high-resolution processing stage while maintaining overall noise reduction effectiveness.
4Measurement precision
If conventional multi-resolution denoising techniques are used, then noise reduction is achieved, but communication bandwidth usage increases
Solution Approach 1:
The patent segments the image data into multiple resolution layers, processing and transmitting only the necessary resolution data at each stage. Instead of transmitting and processing full high-resolution data through entire denoising pipelines, the system segments processing into resolution stages, reducing the quantity of data that needs to be communicated between processing stages while still achieving noise reduction at the final high-resolution output.
Data Source
AI summary
Systems and techniques are described herein for processing image data. For instance, a method for processing image data is provided. The method may include receiving a first plurality of rows of first image data, the first image data representing a field of view at a first resolution; receiving a second plurality of rows of second image data, the second image data representing a portion of the field of view at a second resolution, wherein the first resolution is less than the second resolution; generating processed first image data based on the first plurality of rows of the first image data and a first kernel; generating processed second image data based on the second plurality of rows of the second image data, a second kernel, and the processed first image data; and outputting the processed first image data and the processed second image data.