Super-resolution Video Reconstruction via Hypergraph CNN
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Solution Overview
Problem
Existing super-resolution video reconstruction methods are inefficient in processing dynamic blur and require high computational complexity, often resulting in degraded video quality due to errors propagated from pre-training in optical flow-based approaches.
Innovation Solution
A method involving the extraction of a hypergraph from consecutive video frames, which is then input into a residual convolutional neural network and spatial upsampling network to produce high-resolution frames, preserving time domain information and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If optical flow and motion compensation technology is used to process dynamic blur, then the super-resolution effect is improved, but the computational complexity increases significantly
Solution Approach 1:
The patent segments the video processing task by extracting features from individual frames independently using CNN, avoiding the need for complex optical flow computations across frames. Each frame is processed separately through feature extraction, super-resolution reconstruction, and then combined, dividing the complex temporal-spatial problem into manageable spatial tasks that can be parallelized.
Solution Approach 2:
The patent replaces the mechanical optical flow computation system with a neural network-based feature extraction system. Instead of calculating pixel correspondences through traditional image registration and motion estimation algorithms, the system uses pre-trained CNN models to directly extract and match semantic features, substituting complex mechanical computation with learned representations.
2Manufacturing precision
If optical flow pre-training is used for super-resolution, then motion information is utilized, but errors from pre-training are propagated and degrade the super-resolution effect
Solution Approach 1:
The patent extracts only the essential motion information needed for super-resolution while discarding the error-prone optical flow pre-training process. By using independent frame feature extraction and selective feature matching, the system takes out only the necessary temporal correspondence information without inheriting errors from comprehensive optical flow estimation.
Solution Approach 2:
The patent introduces an intermediate feature representation layer that acts as a mediator between input frames and super-resolution output. Instead of directly using optical flow fields, the system extracts intermediate semantic features through CNN, which then guide the super-resolution process, filtering out errors present in direct optical flow approaches.
3Ease of manufacture
If frame-by-frame super-resolution reconstruction is used, then processing is simple, but dynamic blur cannot be processed and video quality is degraded
Solution Approach 1:
The patent merges multiple frame processing results by combining features from adjacent frames through feature matching and fusion. Instead of processing frames completely independently, the system extracts features from multiple frames, matches corresponding features, and combines the information to reconstruct super-resolution frames, merging temporal information with spatial processing.
Solution Approach 2:
The patent introduces dynamic adaptivity by selectively utilizing features from different frames based on motion characteristics. The system dynamically adjusts which frame features to use for reconstruction based on detected motion patterns, allowing the processing to adapt to different video content and motion scenarios rather than applying a static frame-by-frame approach.
Data Source
AI summary
A super-resolution video reconstruction method, device, apparatus and a computer-readable storage medium are provided. The method includes: extracting a hypergraph from consecutive frames of an original video; inputting a hypergraph vector of the hypergraph into a residual convolutional neural network to obtain an output result of the residual convolutional neural network; and inputting the output result of the residual convolutional neural network into a spatial upsampling network to obtain a super-resolution frame, wherein a super-resolution video of the original video is formed by multiple super-resolution frames.