Neural Network Filter Kernel Warping for Image Reconstruction
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Solution Overview
Problem
Current image reconstruction methods, particularly those using neural networks, face challenges in efficiently updating filter kernels for improved image quality across frames, leading to suboptimal performance in smoothing jagged boundaries and maintaining high-resolution images.
Innovation Solution
The method involves determining an image warping result using change-data between rendered images, estimating a current filter kernel by warping a previous filter kernel, and using this kernel with a second neural network model for image reconstruction, allowing for distributed processing and conditional generation of filter kernels to optimize image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If filter kernels are updated for every frame using neural networks, then image reconstruction quality improves, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent pre-calculates and stores filter kernels for multiple possible rendering resolutions before runtime. During actual image reconstruction, the system simply selects and applies the pre-computed filter kernel corresponding to the current rendering resolution, avoiding the need for real-time neural network computation of filter kernels.
Solution Approach 2:
The patent creates multiple copies of filter kernels for different rendering resolutions and stores them in a lookup table. Instead of generating new filter kernels dynamically, the system copies and applies the appropriate pre-computed filter kernel from the table based on the current rendering resolution, significantly reducing computational overhead.
2Manufacturing precision
If filter kernels are updated frequently, then image quality across frames improves, but processing time increases
Solution Approach 1:
Filter kernels are pre-computed and stored in advance for various rendering resolutions. During frame processing, the system only needs to retrieve and apply the appropriate pre-computed filter kernel, eliminating the time-consuming neural network computation step during frame-by-frame processing.
Solution Approach 2:
The system dynamically selects the appropriate filter kernel from the pre-computed set based on the current rendering resolution, allowing adaptive filtering without the computational overhead of dynamic filter generation. The filter kernel selection changes dynamically with rendering resolution while the actual filter computation remains static.
3Manufacturing precision
If neural networks are used for image reconstruction, then image quality improves, but computational resources required increase
Solution Approach 1:
The patent extracts the computationally intensive filter kernel generation step from the real-time image reconstruction pipeline. The neural network is used only during an offline pre-processing stage to generate and store filter kernels, while the online reconstruction process uses simple lookup and application operations, separating heavy computation from time-sensitive processing.
Solution Approach 2:
The computationally expensive neural network computations are performed in advance during a pre-processing stage, and the results are stored for reuse. This shifts the computational burden from the time-critical rendering pipeline to an offline preparation phase, reducing real-time resource requirements.
4Productivity
If filter kernels are conditionally generated based on rendering resolution, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The patent creates a universal lookup table structure that stores filter kernels for multiple rendering resolutions in a unified format. The same data structure and retrieval logic serve all resolution requirements, allowing the system to handle different resolutions efficiently without requiring separate complex generation logic for each case.
Data Source
AI summary
An image reconstruction method and apparatus are provided. An image reconstruction method includes determining an image warping result by warping a previous reconstruction result using change-data corresponding to a difference between rendered images, determining a previous filter kernel by executing a first neural network model with a previous rendered image and the image warping result, estimating a current filter kernel by warping the previous filter kernel using the change-data, and determining a current reconstruction result by executing a second neural network model with a current rendered image, the current filter kernel, and the image warping result.