Gaze-Tracked Image Restoration for Faster Foveated Display Processing
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Solution Overview
Problem
Existing image restoration techniques require heavy processing capabilities and fail to enhance image quality rapidly and efficiently, especially when multiple images need to be processed quickly for display, often due to the lack of suitable priors and inefficiencies in neural network-based learning.
Innovation Solution
A display apparatus and method that employs gaze-tracking to differentiate between gaze and peripheral regions within an image, applying varying numbers of iterations of image restoration techniques on these regions to reduce processing burden and enhance image quality efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If heavy processing capabilities are used for image restoration, then image quality enhancement is achieved, but processing speed and efficiency deteriorate
Solution Approach 1:
The image is divided into multiple regions (first region, second region, third region) with different restoration requirements. The processor applies different numbers of restoration iterations to different regions, allowing high-quality restoration where needed while maintaining faster processing in less critical areas, thus resolving the contradiction between image quality and processing speed.
Solution Approach 2:
Different regions of the image are assigned different quality levels based on their importance. The first region receives the highest number of restoration iterations for maximum quality, the second region receives a moderate number of iterations, and the third region receives the fewest iterations. This local differentiation allows the system to optimize overall processing efficiency while maintaining high quality in critical areas.
2Manufacturing precision
If uniform image restoration is applied to the entire image, then consistent quality is achieved, but processing time and computational load increase
Solution Approach 1:
The image is segmented into multiple regions with different restoration iteration counts. This segmentation allows the processor to apply computational resources selectively rather than uniformly, reducing total processing time while maintaining quality consistency in regions that require it most.
Solution Approach 2:
Instead of applying the maximum number of restoration iterations uniformly across the entire image, the system applies partial action (fewer iterations) to certain regions where full restoration is not critical, while applying excessive action (more iterations) to regions where quality is paramount. This balanced approach reduces overall processing time while maintaining acceptable quality consistency.
Data Source
AI summary
Disclosed is a display apparatus with at least one display or projector; a gaze-tracking means; and at least one processor configured to process gaze-tracking data, collected by the gaze-tracking means, to determine a gaze direction of a user; identify a gaze region and a peripheral region within an image that is to be displayed by the at least one display or projector, based on the gaze direction; apply at least one image restoration technique on the image in an iterative manner such that M iterations of the at least one image restoration technique are applied on the gaze region, and N iterations of the at least one image restoration technique are applied on the peripheral region, M being different from N; and control the at least one display or projector to display the image having the at least one image restoration technique applied thereon.