Image Super-Resolution Feature Refinement for Faster Serial Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image super-resolution models run slowly due to unfavorable model structures, which hinder efficient processing of image features during super-resolution tasks.
Innovation Solution
Implement a serial processing approach in the feature processing module, utilizing a feature refinement unit, feature fusion unit, and attention unit to enhance the image super-resolution method, and adjust channel quantities to improve model speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If parallel processing structure is used in the super-resolution model, then the model can process features simultaneously, but the model structure becomes complex and processing speed decreases
Solution Approach 1:
The model is divided into multiple processing stages, each handling specific feature extraction tasks. By segmenting the processing into distinct stages with clear responsibilities, the model achieves efficient serial processing without the complexity of parallel structures, thereby improving processing speed while maintaining manageable model architecture.
Solution Approach 2:
Instead of using parallel processing to achieve speed, the patent inverts the approach by using carefully designed serial processing with feature recycling. The inverted strategy focuses on optimizing the sequence of operations and reusing features across stages, which surprisingly achieves faster processing than complex parallel architectures while reducing structural complexity.
2Productivity
If feature recycling is implemented across processing stages, then processing efficiency improves, but the model structure becomes more complex
Solution Approach 1:
Features extracted in early stages are merged and reused in subsequent processing stages. Instead of重新 extracting features, the model combines previously extracted features with new processing, thereby improving processing efficiency while avoiding the need for separate processing paths that would increase structural complexity.
Solution Approach 2:
The same feature extraction modules serve multiple purposes across different stages. Features are designed to be universally applicable and reusable throughout the processing pipeline, allowing a single set of modules to perform multiple functions across stages, thus improving efficiency without proportionally increasing complexity.
3Measurement precision
If channel quantity is increased in the feature processing module, then feature representation quality improves, but processing speed decreases
Solution Approach 1:
Different channel quantities are used at different processing stages based on local requirements. Early stages use appropriate channel dimensions for initial feature extraction, while later stages adjust channel quantities based on the specific processing needs at each stage, achieving high feature representation quality without uniformly increasing complexity and processing time across all stages.
Solution Approach 2:
The channel quantity is dynamically adjusted across processing stages rather than remaining fixed. The model adaptively changes the number of channels in feature maps as processing progresses, allowing high representation quality where needed while maintaining processing speed by reducing channels when full representation is not necessary.
Data Source
AI summary
An image super-resolution method includes: performing feature extraction on an image to be subjected to super-resolution, to obtain a first image feature; processing the first image feature through a feature processing module to obtain a second image feature, where the feature processing module includes: a feature refinement unit that includes at least one set of first convolutional layer and first activation function layer that are connected in series and is configured to process an input feature of the feature processing module; a feature fusion unit configured to perform additive fusion on the input feature of the feature processing module and an output feature of the feature refinement unit; and an attention unit configured to process an output feature of the feature fusion unit to obtain an output feature of the feature processing module; and generating, based on the first image feature and the second image feature, a super-resolution image.