Channel Attention Sampling for Lightweight Image Processing
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Solution Overview
Problem
Existing image processing technologies require significant computing resources and time, making them unsuitable for devices with limited computational power, such as mobile terminals.
Innovation Solution
A method involving feature map extraction, channel attention vector calculation, and weighted feature map generation using adaptive sampling techniques to reduce computational requirements and improve processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a weighted feature map is generated using traditional attention modules, then image processing accuracy is improved, but computation time and computing resource consumption increase significantly
Solution Approach 1:
The patent segments the feature map processing by dividing channels into groups and selecting representative channels from each group, rather than processing all channels individually. This segmentation reduces the computational complexity of attention module operations while maintaining the essential feature representation needed for accurate image processing.
Solution Approach 2:
The patent extracts only the necessary channel information by selecting representative channels from channel groups using clustering algorithms. This extraction approach obtains the essential features needed for accurate processing without computing attention weights for all channels, thereby reducing computation time while preserving accuracy.
2Measurement precision
If traditional image processing methods are used, then processing accuracy is improved, but device overload occurs in devices with limited computing resources
Solution Approach 1:
The patent applies partial action by processing only a subset of channels (representative channels selected from channel groups) rather than all channels. This partial processing reduces the computational load on devices with limited resources, preventing device overload and ensuring stable operation while maintaining sufficient processing accuracy through the selective retention of important channel information.
3Loss of information
If sampling is performed on all channels of the feature map, then processing completeness is improved, but computing resource consumption increases
Solution Approach 1:
The patent creates a simplified copy of the feature map by selecting representative channels that replicate the essential information from each channel group. This copying approach maintains processing completeness by preserving key features while significantly reducing computing resource consumption by working with a smaller subset of channels instead of the complete feature map.
Data Source
AI summary
A lightweight image processing method is provided. The method may be performed by one or more processors, and include extracting a feature map from an image, calculating a channel attention vector based on the feature map, generating a weighted feature map based on the feature map and based on the channel attention vector, inputting the weighted feature map to an image processing model to generate a processed image. The image processing model may include an artificial neural network model.