Video Super-Resolution With Recurrent Structure-Detail Features
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing super-resolution technologies suffer from delays and complex feature extraction processes when processing real-time video streams, leading to reduced user experience due to the need for buffering multiple frames and complex network operations.
Innovation Solution
The method decomposes an image into structure and detail sub-images, fuses hidden state information with these sub-images, and performs iterative feature extraction to enrich the features, enabling high-resolution image generation without frame buffering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple adjacent frames are buffered and stored for super-resolution processing, then the super-resolution quality is improved, but the processing delay increases
Solution Approach 1:
The patent segments the processing of multiple frames into independent processing units, where each frame can be processed individually using a recurrent neural network. This allows the system to process frames sequentially rather than requiring all frames to be buffered before processing, thereby reducing delay while maintaining quality through the recurrent architecture that can leverage historical frame information.
2Manufacturing precision
If a complex feature extraction network is used to process 2N+1 frames simultaneously, then the super-resolution quality is improved, but the device complexity increases
Solution Approach 1:
The patent divides the complex feature extraction task into separate processing streams using a recurrent neural network architecture. Instead of processing all 2N+1 frames through a single complex network simultaneously, the system processes frames sequentially through simpler recurrent units, reducing overall network complexity while maintaining the ability to extract features from multiple frames.
Solution Approach 2:
The patent employs a dynamic recurrent neural network that can adaptively process variable numbers of frames (2N+1) without requiring a fixed complex architecture. The recurrent structure allows the network to dynamically adjust its processing based on the number of available frames, simplifying the overall device complexity compared to a static complex network designed to handle all frames simultaneously.
3Manufacturing precision
If 2N+1 frames are processed simultaneously through motion compensation and alignment, then the super-resolution quality is improved, but the processing time increases
Solution Approach 1:
The patent segments the simultaneous processing of 2N+1 frames into sequential processing steps using a recurrent neural network. Each frame is processed in sequence through motion compensation and alignment operations, allowing the system to maintain quality while improving processing speed by avoiding the computational burden of simultaneous multi-frame processing.
Data Source
AI summary
An image processing method and apparatus in the field of artificial intelligence are disclosed. The method comprises: decomposing a first image to obtain a first structure sub-image and a first detail sub-image, where the first image is any frame of image in video data other than a first frame; fusing first hidden state information and the first structure sub-image to obtain a second structure sub-image, and splicing the first hidden state information and the first detail sub-image to obtain a second detail sub-image; performing feature extraction based on the second structure sub-image and the second detail sub-image to obtain a structure feature and a detail feature; and obtaining an output image based on the structure feature and the detail feature, where a resolution of the output image is higher than a resolution of the first image.