Guided Neural Image Processing With Guidance Maps and GPU Acceleration
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Solution Overview
Problem
Current graphics processing methods, particularly in gaming and animation, struggle with high performance image processing due to a lack of effective guidance in Deep Neural Network (DNN) methods, making it difficult for end users to achieve high-quality results in image rendering tasks.
Innovation Solution
A guided neural network model is employed to generate neural guidance maps that provide direction for image processing, utilizing a graphics processing unit (GPU) to accelerate operations and enhance image rendering through techniques like style transfer and compact convolution operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If Deep Neural Network methods are used for image processing, then ease of use is improved, but manufacturing precision deteriorates due to lack of effective guidance
Solution Approach 1:
The patent introduces guidance maps as intermediary structures that mediate between the simple DNN operations and the complex image rendering requirements. These guidance maps contain structured information about style, lighting, and geometry that guide the DNN to produce high-quality results while maintaining ease of use.
Solution Approach 2:
The patent performs preliminary processing to generate guidance maps before the main image rendering operation. These pre-computed guidance maps contain essential information about the desired output characteristics, enabling the DNN to achieve high precision without requiring complex user input or post-processing.
2Manufacturing precision
If traditional mathematical models and shaders are used for image processing, then manufacturing precision is improved, but device complexity worsens due to difficulty in model building
Solution Approach 1:
The patent replaces the manual mechanical process of building mathematical models and designing shaders with an automated neural network-based system. The DNN automatically learns the appropriate transformations from example pairs, eliminating the need for users to manually construct complex mathematical models while maintaining high rendering quality.
Solution Approach 2:
The patent changes the fundamental parameters of the image processing system by transitioning from explicit mathematical model specifications to implicit neural network representations. This allows the system to achieve high precision through learned parameters rather than manually designed mathematical models, reducing complexity while maintaining or improving quality.
3Productivity
If parallel processing techniques are used to increase performance, then productivity is improved, but measurement precision deteriorates due to synchronization challenges in SIMT architecture
Solution Approach 1:
The patent segments the image processing task into independent pixel-level operations that can be executed in parallel without requiring complex synchronization. Each pixel or small region can be processed independently using the guidance maps, allowing full utilization of parallel processing capabilities while maintaining precision through the structured guidance information.
Data Source
AI summary
The present disclosure provides an apparatus and method of guided neural network model for image processing. An apparatus may comprise a guidance map generator, a synthesis network and an accelerator. The guidance map generator may receive a first image as a content image and a second image as a style image, and generate a first plurality of guidance maps and a second plurality of guidance maps, respectively from the first image and the second image. The synthesis network may synthesize the first plurality of guidance maps and the second plurality of guidance maps to determine guidance information. The accelerator may generate an output image by applying the style of the second image to the first image based on the guidance information.