Image Processing with Spatial-Channel Attention for Critical Detail Recovery
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Solution Overview
Problem
Current image processing technologies face challenges in accurately representing critical details in super-resolution images required for downstream tasks such as medical imaging and self-driving applications.
Innovation Solution
An image processing device and method that utilizes a super-resolution model with spatial and channel attention models to enhance the weight of regions of interest in images, employing neural network blocks with squeeze and excitation convolution networks to improve super-resolution processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional super-resolution processing is applied to improve image clarity, then the overall image resolution is enhanced, but the critical details required for downstream tasks are not accurately represented
Solution Approach 1:
The patent applies local quality by introducing spatial attention mechanisms that selectively enhance different regions of the image with different weights. The attention map dynamically identifies and emphasizes regions containing critical details for downstream tasks while applying different enhancement levels to different spatial locations, thereby achieving both overall clarity improvement and accurate representation of critical details simultaneously
2Measurement precision
If comprehensive super-resolution processing is applied to all image regions, then overall image quality is improved, but computational resources are wasted on regions with less important features
Solution Approach 1:
The patent implements local quality by applying region-specific processing through attention mechanisms. The spatial attention model generates attention maps that identify important regions and apply enhanced processing only to those areas, while reducing processing intensity in less important regions. This selective approach maintains overall image quality while significantly reducing computational resource consumption by avoiding uniform comprehensive processing across the entire image
3Manufacturing precision
If multiple neural network blocks are used to enhance critical details, then the representation accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the super-resolution task into distinct functional modules: a spatial attention model for regional importance identification, a channel attention model for feature channel weighting, and multiple neural network blocks with specific functions. This modular segmentation allows each component to be optimized independently and contributes to improved critical details representation while managing overall system complexity through structured organization
Solution Approach 2:
The patent implements dynamics through the use of dynamically generated attention maps that adapt to the specific input image content. The spatial and channel attention mechanisms adjust their weighting in real-time based on the features present in each image, allowing the network structure to be flexible and adaptive rather than static, which improves representation accuracy without requiring a fixed complex architecture for all possible inputs
Data Source
AI summary
An image processing device is provided, which includes an image capture circuit and a processor. The image capture circuit is configured to capture a low-resolution image. The processor is connected to the image capture circuit and executes a super-resolution model (SRM), wherein the SRM includes multiple neural network blocks, and the processor is configured to perform the following operations: generating a super-resolution image from the low-resolution image by using the multiple neural network blocks, where one of the multiple neural network blocks includes a spatial attention model (SAM) and a channel attention model (CAM), the CAM is concatenated after the SAM, and the SAM and the CAM are configured to enhance a weight of a region in the super-resolution image, which is covered by a region of interest in the low-resolution image. In addition, an image processing method is also disclosed herein.