3D Convolutional Video Super-Resolution for Detail and Jitter
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Solution Overview
Problem
Existing video image super-resolution methods fail to perform effective feature analysis on input images, leading to output images lacking details and suffering from video inter-frame jitter, with longer operation times.
Innovation Solution
A method involving multiple frames of input data, where feature extraction is performed using a first three-dimensional convolutional layer, followed by down-sampling, convolution to merge frames, and up-sampling at a preset multiple, allowing for improved feature analysis and reducing operation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional image interpolation method is used for video super-resolution, then the operation time is short, but the image details and edges are lost during amplification
Solution Approach 1:
The patent segments the video processing into multiple independent 3D convolutional layers, each responsible for specific feature extraction tasks. This segmentation allows parallel processing of different feature types (spatial, temporal, motion) while maintaining detailed image information, resolving the contradiction between preserving image details and reducing operation time.
Solution Approach 2:
The patent transitions from traditional 2D image processing to 3D convolutional processing by incorporating the temporal dimension. This dimensional change enables simultaneous processing of spatial features and temporal motion information, preserving image details while improving processing efficiency through unified 3D feature extraction.
2Reliability
If existing super-resolution methods are used, then the processing speed is fast, but video inter-frame jitter occurs and image quality deteriorates
Solution Approach 1:
The patent implements continuous temporal feature extraction across multiple frames using 3D convolutional layers, ensuring that motion information is consistently captured throughout the video sequence. This continuous processing maintains video stability by preventing inter-frame jitter while preserving processing speed through efficient temporal feature reuse.
Solution Approach 2:
The patent incorporates feedback mechanisms where extracted motion features and temporal information are fed back into subsequent processing stages. This feedback loop allows the system to continuously refine frame alignment and motion compensation, maintaining video stability without sacrificing processing speed through iterative optimization.
3Manufacturing precision
If multiple frames are processed individually, then image details are preserved, but operation time increases significantly
Solution Approach 1:
The patent merges multiple frame processing operations into a unified 3D convolutional network that processes multiple frames simultaneously. By combining spatial and temporal processing in a single integrated architecture, the system preserves image details from individual frames while achieving efficient batch processing that reduces overall operation time.
Solution Approach 2:
The 3D convolutional layers serve multiple functions simultaneously: they extract spatial features, temporal features, and motion information from multiple frames in a single operation. This multi-functionality allows the system to preserve detailed image information across frames while maintaining high processing efficiency through a universal processing architecture.
Data Source
AI summary
Provided are a method for enhancing a video resolution enhancement, a computer readable storage medium, and an electronic device. The method includes: obtaining multiple image frames as input data, and obtaining initial data by performing feature extraction on the input data using a first three-dimensional convolutional layer; obtaining first feature data by performing down-sampling on the initial data at a preset multiple; obtaining first reference data by performing a convolution operation on the first feature data using a second three-dimensional convolutional layer to merge the first feature data as one frame; and obtaining first output data by performing up-sampling on the first reference data at the preset multiple.