Neural Network Upsampling via Denoised Low-Res Input
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Solution Overview
Problem
Generating high-quality video requires significant computational resources, making effective processing challenging due to the large amount of information involved, and existing methods struggle to efficiently enhance or process video quickly.
Innovation Solution
A neural network-based approach that uses both noisy and denoised versions of low-resolution images to generate high-resolution images, leveraging denoising and upsampling techniques to reduce computational demands while maintaining image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional video processing methods are used to generate high-quality video, then image quality can be maintained, but computational resources and processing time increase significantly
Solution Approach 1:
The patent applies preliminary action by performing denoising on the low-resolution image before upsampling. The denoised image serves as a cleaner input for the neural network, which helps the network learn better pixel placement patterns and generates higher quality high-resolution images more efficiently. This preprocessing step improves the overall processing pipeline's effectiveness while managing computational resources.
2Manufacturing precision
If high-resolution video is processed directly, then image quality is maintained, but computational resources and memory usage increase significantly
Solution Approach 1:
The patent applies segmentation by separating the video processing task into distinct stages: denoising the low-resolution image, then upsampling to high-resolution. This segmentation allows each stage to be optimized independently, reducing the overall computational burden compared to processing high-resolution video directly from scratch.
Solution Approach 2:
The patent uses a neural network trained on denoised low-resolution images to generate high-resolution images. Instead of processing actual high-resolution video data, the system creates a synthetic copy through the neural network, which requires significantly fewer computational resources and memory while maintaining image quality.
3Measurement precision
If denoising is performed before upsampling, then neural network can infer pixel placement more accurately, but additional processing step is required
Solution Approach 1:
The patent introduces an intermediary step (denoising) between the original low-resolution image and the upsampling process. The denoised image acts as an intermediary that provides cleaner input data to the neural network, enabling more accurate pixel placement inference. Although this adds a processing step, it significantly improves the quality of the final high-resolution output.
Data Source
AI summary
Apparatuses, systems, and techniques to use one or more neural networks to generate an upsampled version of one or more images based, at least in part, on a denoised version of said one or more images. At least one embodiment pertains to generating an upsampled high-resolution image from a noisy version and denoised version of a low-resolution image. At least one embodiment pertains to separating components of a low-resolution image before denoising an image.