Spatial Domain Deconvolution for Real-Time Projection Resolution
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Solution Overview
Problem
Existing projection systems face challenges in enhancing display resolution due to limited hardware capabilities, particularly in real-time implementation of frequency-domain optical aberration correction, and existing super-resolution methods are either computationally expensive or limited in handling spatially variant degradation models.
Innovation Solution
A spatial-based filter system that uses a spatial domain deconvolution operation with a spatial domain Wiener filter, derived from estimating the point spread function of the projector, to approximate frequency-domain optical corrections, enabling real-time resolution enhancement by upsampling and downsampling high-resolution signals into multiple low-resolution signals for projection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequency-domain optical aberration correction is implemented, then resolution enhancement is achieved, but computational complexity increases and real-time implementation becomes difficult
Solution Approach 1:
The patent replaces the complex frequency-domain filtering system with a simplified spatial-domain convolution system. Instead of using FFT-based frequency domain operations which require significant computational resources, the invention uses direct spatial domain convolution with pre-computed kernels, dramatically reducing computational complexity while maintaining resolution enhancement capabilities
Solution Approach 2:
The patent performs preliminary computation of convolution kernels offline based on the projector's point spread function. These pre-computed kernels are then reused in real-time processing, eliminating the need for complex real-time frequency domain calculations and enabling real-time implementation
2Adaptability or versatility
If spatial domain SR methods with a-priori constraints are used, then handling of spatially variant degradation models improves, but computational expense increases
Solution Approach 1:
The patent applies different convolution kernels to different spatial locations based on the spatially variant point spread function. Each location receives a customized kernel that accounts for local degradation characteristics, enabling accurate handling of spatially variant models without requiring complex iterative optimization at each location
Solution Approach 2:
The patent replaces computationally expensive iterative spatial domain optimization methods with direct spatial convolution using pre-computed kernels. This substitution maintains the ability to handle spatially variant degradation while dramatically reducing computational expense to enable real-time processing
3Reliability
If iterative back-projection methods are employed, then a-priori constraints can be incorporated, but real-time application becomes infeasible
Solution Approach 1:
The patent incorporates a-priori constraints (such as edge-preserving image priors) during the offline computation of the convolution kernels. Once these constraints are applied and kernels are computed, the actual super-resolution processing requires only simple spatial convolution operations that can be executed in real-time without iterative optimization
Solution Approach 2:
The patent replaces iterative back-projection optimization with direct spatial convolution. The complex iterative process is substituted by pre-computed convolution kernels that encode the desired a-priori constraints, transforming an infeasible real-time process into a simple, fast convolution operation
Data Source
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AI summary
A projection system for projecting an image with an increased apparent resolution is provided. The projection system includes one or more projectors, a resampler module and a deconvolution module. The resampler module is configured to upsample an incoming high-resolution signal, perform an integer shift operation on a signal, and downsample to two or more low-resolution signals. The deconvolution module is configured to filter the upsampled high-resolution signal using a spatial domain deconvolution operation, the spatial domain deconvolution operation approximating frequency domain optical corrections based on characteristics of the one or more projectors. Preferably, the spatial domain deconvolution operation uses an NxN spatial kernel extracted from a spatial domain approximation of a Wiener filter.