Image Super-Resolution With Channel Attention for Lower Compute
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Solution Overview
Problem
Existing image super-resolution methods require high computing costs, making it difficult for devices with limited resources to perform image super-resolution, especially in real-time applications like video conferences.
Innovation Solution
An image super-resolution method utilizing a channel attention mechanism to determine initial weights for channel images, discarding less important images based on a preset weight condition to reduce computing resources and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image super-resolution is performed using conventional methods, then image resolution is improved, but computing cost increases significantly
Solution Approach 1:
The patent extracts and removes redundant channel images from the feature map based on channel attention mechanism. By calculating attention weights for each channel and thresholding them, the system identifies and discards channels with low importance (weights below threshold), retaining only essential channels for super-resolution processing. This extraction of useful information reduces computational complexity while maintaining resolution enhancement effectiveness.
Solution Approach 2:
The patent applies local quality by differentiating the importance of different channel images through channel attention mechanism. Instead of treating all channels uniformly, the system calculates individual attention weights for each channel based on its contribution to feature representation. Channels with higher attention weights are retained while those with lower weights are discarded, creating a selective processing approach that optimizes computing resources based on local channel significance.
2Reliability
If all channel images are processed to maintain comprehensive feature extraction, then image quality is preserved, but computational burden increases
Solution Approach 1:
The patent implements feedback through the channel attention mechanism that continuously evaluates the importance of each channel based on its actual contribution to feature representation. The attention weights are calculated based on channel-specific statistics and used to dynamically determine which channels to retain. This feedback loop ensures that computational resources are allocated to the most informative channels, maintaining image quality while reducing overall computational burden.
Solution Approach 2:
The patent changes the parameter of channel selection by introducing a threshold mechanism for attention weights. Instead of processing all channels with fixed weight, the system dynamically adjusts which channels are processed based on their attention weights relative to the threshold. This parameter change enables adaptive feature selection that balances image quality preservation with computational efficiency.
Data Source
AI summary
An image super-resolution method, a device, and a storage medium are provided. The method includes: determining a super-resolution requirement including a requirement for converting an image with a first resolution into an image with a second resolution greater than the first resolution; and determining a preset image super-resolution network that meets the super-resolution requirement, and inputting a to-be-performed-super-resolution image with the first resolution into the preset image super-resolution network, to obtain a result image with the second resolution output by the preset image super-resolution network.